Few-Shot Text Classification

Az Atışlı Metin Sınıflandırma (Few-Shot)

Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels.

NLP text-classification task (low-resource)

Prototypical networks / MAML / pretrained-model fine-tuning

Output: Class label per document

Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL.

Tunstall, L., Reimers, N., Jo, U.E.S., Bates, L., Korat, D., Wasserblat, M. & Pereg, O. (2022). Efficient Few-Shot Learning Without Prompts. arXiv.

text-classification domain-adaptation-nlp bert-embeddings sentiment-analysis


Feynman Diagram

Feynman diagrams are graphical representations of particle interactions introduced by Richard Feynman in 1949. They provide an intuitive and systematic way to visualize and calculate amplitudes for quantum field theory processes, converting complex mathematical expressions into geometric pictures that reveal the underlying physics.

Feynman, R. P. (1949). The Theory of Positrons. Physical Review, 76(6), 749–759.

Feynman, R. P. (1961). Quantum Electrodynamics. Addison-Wesley.

Peskin, M. E., & Schroeder, D. V. (1995). An Introduction to Quantum Field Theory. Addison-Wesley.


FF-ARAS

FF-ARAS (Fermatean extension of FF-ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)

Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


FF-COCOSO

FF-COCOSO (FF-CoCoSo — Fermatean extension of FF-COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-CODAS

FF-CODAS (Fermatean extension of FF-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-COPRAS

FF-COPRAS (Fermatean extension of FF-COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-EDAS

FF-EDAS (Fermatean extension of FF-EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-GRA

FF-GRA (Fermatean extension of FF-GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-MABAC

FF-MABAC (Fermatean extension of FF-MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-MARCOS

FF-MARCOS (Fermatean extension of FF-MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-MOORA

FF-MOORA (Fermatean extension of FF-MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-PROMETHEE

FF-PROMETHEE (2-tuple Linguistic Fermatean Fuzzy PROMETHEE (Akram-Bibi 2023)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Bibi, R. in 1985 crisp; 2023 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fermatean fuzzy outranking — 2-tuple linguistic Fermatean fuzzy sets (2TLFFS), μ³+ν³ ≤ 1

Akram, M., Bibi, R. (2023). Multi-criteria group decision-making based on an integrated PROMETHEE approach with 2-tuple linguistic Fermatean fuzzy sets. Granular Computing


FF-SAW

FF-SAW (Fermatean extension of FF-SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-TODIM

FF-TODIM (Fermatean extension of FF-TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-TOPSIS

FF-TOPSIS (Fermatean extension of FF-TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-VIKOR

FF-VIKOR (Fermatean extension of FF-VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-WASPAS

FF-WASPAS (Fermatean extension of FF-WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FF-WPM

FF-WPM (Fermatean extension of FF-WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


FIQ

FIQ
Fibromyalgia Impact
Fibromyalgia Questionnaire

Rheumatologic and Chronic Pain Conditions

The FIQ is the most widely used patient-reported outcome measure for fibromyalgia disease burden. Developed by Cynthia Burckhardt and colleagues in 1991, this 10-item questionnaire quantifies how fibromyalgia affects physical function, work capacity, depression, anxiety, sleep, pain, and fatigue. The revised version (FIQR, 21 items) offers enhanced psychometric properties and is the current standard in fibromyalgia clinical trials and practice.

Self-report symptom and functional impairment questionnaire

Burckhardt, C. S., Clark, S. R., & Bennett, R. M. (1991). The Fibromyalgia Impact Questionnaire: Development and validation. The Journal of Rheumatic Diseases, 18(5), 728-735.

Bennett, R. M., Friend, R., Jones, K. D., Ward, R., Han, B. K., & Ross, R. L. (2009). The Revised Fibromyalgia Impact Questionnaire (FIQR): Validation and psychometric properties. Seminars in Arthritis and Rheumatism, 39(6), 448-453.

Häuser, W., Petzke, F., Üçeyler, N., & Köllner, V. (2012). Fibromyalgia. Nature Reviews Disease Primers, 1, 15022.

eortc-qlq-c30 dlqi chronic-heart-failure-questionnaire kidney-disease-quality-of-life


Fick's Laws

Fick's Laws of Diffusion for Mass Transfer

Fick's Laws describe how species diffuse through media due to concentration gradients. The First Law (steady-state) relates diffusion flux to concentration gradient, while the Second Law (transient) describes how concentration changes over time. These laws are fundamental to mass transfer analysis, applying to gases, liquids, and solids. Fick's Laws are analogous to Fourier's Law of heat conduction, replacing temperature with concentration.

Fick, A. (1855). On liquid diffusion. Philosophical Magazine, 10(63), 30-39.

Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.


Fidelity Assessment in Implementation

Fidelity Assessment: Measuring the Degree to Which Interventions Are Delivered as Intended in Real-World Implementation

fidelity
treatment fidelity
protocol adherence
implementation fidelity

National Institutes of Health Behavior Change Consortium; Bellg et al.

Fidelity Assessment is the systematic measurement of the degree to which an intervention is delivered as designed in real-world practice. Formalized by the National Institutes of Health Behavior Change Consortium (Bellg et al. 2004) and expanded in MRC guidance (Moore et al. 2015), fidelity assessment is critical to implementation science because it answers: 'Did we deliver the intervention correctly?' A clinical trial may show a treatment works, but if delivered poorly in practice, benefits disappear. Fidelity assessment prevents misattribution of failure (was the intervention weak, or was implementation poor?) and guides coaching to improve quality.

Bellg, A. J., Borrelli, B., Resnick, B., Hecht, J., Minicucci, D. S., Ory, M., ... & Treatment Fidelity Workgroup of the National Institutes of Health Behavior Change Consortium. (2004). Enhancing treatment fidelity in health behavior change studies: Best practices and recommendations from the NIH Behavior Change Consortium. Health Psychology, 23(5), 443-451.

Moore, G. F., Audrey, S., Barker, M., Bond, L., Bonell, C., Hardeman, W., ... & Baird, J. (2015). Process evaluation of complex interventions: Medical Research Council guidance. BMJ, 350, h1258.

Schoenwald, S. K., & Garland, A. F. (2013). A review of treatment adherence measurement methods. Psychological Assessment, 25(1), 146-156.

implementation-outcome-taxonomy re-aim-framework normalization-process-theory cfir-framework scaling-up-interventions


Fidelity Scale

Fidelity Scale
Implementation Fidelity
Fidelity Measurement

Carroll, C.; Patterson, M.; and colleagues; Goodman, C.; and others

Fidelity of Implementation refers to the degree to which an evidence-based practice or intervention is delivered as originally designed and intended. The Fidelity of Implementation Scale (or fidelity assessment framework) operationalizes this concept by specifying the core components of an intervention, defining each component precisely, and then assessing whether practitioners deliver each component when appropriate. Fidelity is distinct from adoption (whether staff use the innovation) and outcomes (whether the innovation produces the intended benefit). An innovation can be widely adopted but delivered with low fidelity (incorrectly or incompletely), often resulting in poor outcomes. Conversely, perfect fidelity without adaptation may fail in some contexts. Fidelity monitoring is essential in implementation science to understand whether implementation failures stem from ineffective interventions (true lack of efficacy) or ineffective delivery (low fidelity despite effective intervention). Fidelity assessment uses observation, checklist, and record review methods tailored to the intervention type.

Observational and performance-based assessment

Goodman, C., & Evans, C. (2010). Audit of the use of the Measure of Processes by Area Teams (MOPAT) in the acute hospital setting. Journal of Clinical Nursing, 19(11-12), 1514–1524.

Carroll, C., Patterson, M., Wood, S., Booth, A., Rick, J., & Balain, S. (2007). A conceptual framework for implementation fidelity. Implementation Science, 2, 40.

normalisation-measure-development evidence-based-practice-attitude implementation-leadership-scale knowledge-to-action-scale stages-of-concern-questionnaire


Field-based autoethnography

field autoethnography
site-based autoethnography
embodied field autoethnography
FBAE

Ellis, Adams, and Bochner; building on autoethnography foundations by Carolyn Ellis and Arthur Bochner

Field-based autoethnography is a qualitative research design in which the researcher immerses themselves in a specific physical or social setting and draws on their own lived experience within that field to produce analytically reflexive accounts. It blends the systematic observational practices of ethnographic fieldwork with the first-person introspective voice of autoethnography, generating knowledge that is simultaneously personal, cultural, and contextually grounded.

Fieldnotes, personal memos, observations, reflexive journal entries, artifacts

Ellis, C., Adams, T. E., & Bochner, A. P. (2011). Autoethnography: An overview. Forum: Qualitative Social Research, 12(1), Art. 10.

Angrosino, M. (2007). Doing Ethnographic and Observational Research. Sage.

autoethnography ethnography participatory-ethnography narrative-inquiry interpretive-autoethnography field-based-ethnography


Field-based case study

fieldwork case study
naturalistic case study
in-situ case study
field case study

1970s–1990s (formalized by Yin 1984, Stake 1995)

Robert Yin, Robert Stake (case study formalization); field-based tradition rooted in anthropological and sociological fieldwork

A field-based case study is a qualitative research design that investigates a bounded phenomenon — a case — within its real-world, natural setting through sustained on-site data collection. Combining the analytical structure of case study methodology with the direct observational immersion of fieldwork, it enables rich, context-sensitive understanding of how phenomena unfold in practice. The approach is firmly grounded in the frameworks of Robert Yin and Robert Stake and draws on anthropological traditions of participant and non-participant observation.

Field observations, interviews, documents, artifacts collected on-site in natural settings

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Stake, R. E. (1995). The Art of Case Study Research. Sage.

case-study ethnography field-based-ethnography participant-observation multiple-case-study narrative-inquiry


Field-based classic grounded theory

Field-Based Classic (Glaserian) Grounded Theory

Glaserian grounded theory in naturalistic settings

1967 (Glaser & Strauss); field-based application codified from late 1970s onward

Barney G. Glaser (classic GT); field-based variant draws on naturalistic inquiry traditions

Field-based classic grounded theory applies Barney Glaser's original (Glaserian) grounded theory method within naturalistic, in-situ settings — combining sustained field immersion with the classic GT emphasis on emergence, theoretical sensitivity, and the constant comparative method. The researcher enters the social scene without a predetermined framework, collects data through observation and naturalistic interviews, and allows a substantive theory to surface inductively from the field rather than imposing conceptual structure in advance.

Field observations, naturalistic interviews, documents, field notes

Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.

Glaser, B. G. (1992). Basics of Grounded Theory Analysis: Emergence vs. Forcing. Sociology Press.

classic-grounded-theory grounded-theory field-based-grounded-theory constructivist-grounded-theory ethnography field-based-ethnography


Field-based cluster sampling

field cluster sampling
in-field cluster sampling
area cluster sampling (field)
field survey cluster design

1950s (theory); 1970s–1980s (field survey practice)

William G. Cochran (theoretical foundations); WHO EPI programme (field application)

Field-based cluster sampling is a probability sampling method in which naturally occurring geographic or administrative groups (clusters) are first randomly selected, and then data are collected in person from units within those clusters. It is the standard design for large-scale field surveys in public health, agriculture, education, and humanitarian response, where compiling a full population list is impractical but clusters such as villages, schools, or census tracts can be identified and physically accessed.

Cross-sectional survey data collected in natural or field settings

World Health Organization. (1991). Training for mid-level managers: The EPI coverage survey. WHO/EPI/MLM/91.10. World Health Organization.

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.

cluster-sampling multistage-sampling systematic-sampling simple-random-sampling stratified-sampling field-based-stratified-sampling


Field-Based Constructivist Grounded Theory

field-based CGT
constructivist GT with fieldwork
situated grounded theory
Charmaz-field grounded theory

2000s (Charmaz 2006; fully articulated by 2014)

Kathy Charmaz (constructivist variant); fieldwork orientation drawn from symbolic interactionist tradition

Field-based constructivist grounded theory integrates Kathy Charmaz's constructivist grounded theory with active fieldwork in natural settings. Rather than relying solely on retrospective interviews, the researcher enters the participants' world — observing, interacting, and collecting data where social processes unfold — while simultaneously coding and building theory. The result is a grounded substantive theory that is both empirically anchored in real contexts and epistemologically co-constructed between researcher and participants.

Qualitative research design and analytic approach

Interviews, field observations, documents, field notes

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.

Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.

grounded-theory ethnography constructivist-grounded-theory situational-analysis focused-ethnography thematic-analysis


Field-based Content Analysis

field content analysis
naturalistic content analysis
ethnographic content analysis
ECA

Field-based content analysis is a qualitative analytic approach that systematically examines documents, artifacts, and texts encountered or produced within a natural field setting. Originally formulated by David Altheide as ethnographic content analysis (ECA), it blends the systematic rigor of traditional content analysis with the reflexive, iterative logic of ethnographic inquiry, allowing the researcher to interact continuously with the data and revise analytic categories as new meaning emerges from the field.

Field documents, artifacts, media texts, observational records, archival materials

Altheide, D. L. (1987). Ethnographic content analysis. Qualitative Sociology, 10(1), 65–77.

Altheide, D. L. (1996). Qualitative Media Analysis. Sage Publications.

thematic-analysis ethnography document-analysis content-analysis grounded-theory discourse-analysis


Field-based convenience sampling

intercept sampling
on-site convenience sampling
street intercept sampling
field intercept survey

Conventional practice in social and epidemiological field research

Field-based convenience sampling is a non-probability technique in which researchers recruit participants by approaching whoever is physically present and accessible at a chosen real-world location — a market, hospital waiting room, park, or transit hub. It is widely used in public health surveillance, marketing research, and exploratory social surveys when rapid, low-cost data collection is needed and probability sampling is not feasible.

Survey or interview data collected in person at a physical location

Babbie, E. (2010). The Practice of Social Research (12th ed.). Wadsworth Cengage Learning.

convenience-sampling purposive-sampling quota-sampling snowball-sampling systematic-sampling field-based-quota-sampling


Field-based Conversation Analysis

field CA
naturalistic conversation analysis
in situ conversation analysis
fieldwork CA

1960s–1970s (CA foundations); field applications consolidated from the 1990s onward

Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA roots); extended to field settings by later ethnomethodologists

Field-based conversation analysis (field CA) applies the rigorous sequential-analytic methods of conversation analysis to talk and interaction recorded in real-world settings — workplaces, clinics, classrooms, and public spaces — rather than to pre-existing corpora or laboratory data. By combining sustained fieldwork access with fine-grained transcript analysis, it reveals how social order is accomplished turn by turn in the actual environments where it matters.

Audio or video recordings of naturally occurring talk in situ; field notes

Hindmarsh, J., & Llewellyn, N. (2017). Video in sociomaterial investigations: A solution to the problem of relevance for organizational research. Organizational Research Methods, 21(2), 412–437.

Heritage, J. (1984). Garfinkel and Ethnomethodology. Polity Press.

conversation-analysis ethnomethodology discourse-analysis ethnography interaction-analysis multimodal-interaction-analysis


Field-based Critical Discourse Analysis

Field-theoretic CDA
Bourdieusian CDA
sociological CDA
field-oriented discourse analysis

Norman Fairclough and Lilie Chouliaraki (synthesis with Bourdieu's field theory)

Field-based Critical Discourse Analysis (Field-based CDA) integrates Pierre Bourdieu's sociological concept of the field — structured social spaces with their own rules, capital, and positions — with the linguistic and critical tools of Critical Discourse Analysis. The approach examines how language constructs, legitimates, and contests power relations within specific institutional or social fields, situating texts in their broader sociological context rather than treating discourse in isolation.

Qualitative critical discourse framework

Texts, documents, interviews, ethnographic field notes

Bourdieu, P. (1991). Language and Symbolic Power. Harvard University Press.

Chouliaraki, L., & Fairclough, N. (2001). Discourse in Late Modernity: Rethinking Critical Discourse Analysis. Edinburgh University Press.

critical-discourse-analysis discourse-analysis thematic-analysis ethnography institutional-ethnography social-semiotics


Field-based Deviant Case Sampling

field deviant case sampling
outlier case sampling in field research
extreme case sampling in fieldwork
in-situ deviant case sampling

1980s–1990s (purposive/deviant case sampling literature)

Michael Quinn Patton; Yvonna Lincoln & Egon Guba

Field-based deviant case sampling is a purposive strategy that deliberately selects cases deviating markedly from an established pattern or norm, with data collected through direct fieldwork — observation, in-situ interviews, and ethnographic engagement — in the participants' natural settings. By studying outliers on-site, researchers gain contextually grounded insight into why and how certain cases diverge from the typical pattern.

Observational field notes, in-situ interviews, ethnographic records

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage Publications.

deviant-case-sampling purposive-sampling maximum-variation-sampling typical-case-sampling field-based-purposive-sampling snowball-sampling


Field-based digital ethnography

connective ethnography
blended digital ethnography
hybrid online-offline ethnography
field-integrated digital ethnography

Field-based digital ethnography is a qualitative research design that combines traditional in-person fieldwork with systematic collection and analysis of digital data. Rather than studying online communities in isolation, it traces how social life moves between physical settings and digital spaces, treating both as equally real sites of cultural practice. Rooted in Christine Hine's virtual ethnography and Sarah Pink's digital ethnography principles, it is particularly suited to studying communities whose practices span offline and online worlds.

In-person fieldwork observations, online digital traces, interviews, digital artifacts

Hine, C. (2000). Virtual Ethnography. Sage.

Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.

digital-ethnography netnography ethnography participatory-digital-ethnography longitudinal-ethnography visual-elicitation-ethnography


Field-based Discourse Analysis

field discourse analysis
Bourdieusian discourse analysis
sociological discourse analysis
FDA

Synthesised from Pierre Bourdieu's field theory and discourse analysis; systematised by researchers including John Frow

Field-based discourse analysis integrates Pierre Bourdieu's sociological concept of the field — a structured social space of positions, capital, and struggle — with the close textual methods of discourse analysis. Rather than treating language as a neutral medium, it examines how discourse is produced, circulated, and received within specific social fields (education, law, journalism, science, etc.), and how discursive choices reflect and reproduce the distribution of power and capital within those fields.

Texts, documents, interviews, field notes, media materials

Frow, J. (1985). Discourse and Power. Economy and Society, 14(2), 193–214.

critical-discourse-analysis discourse-analysis thematic-analysis ethnography narrative-analysis grounded-theory


Field-based Document Analysis

FBDA
field document analysis
naturalistic document analysis
ethnographic document analysis

1970s–1980s (codified in qualitative research methodology)

Rooted in ethnographic fieldwork traditions; systematised in qualitative education research by Bogdan & Biklen and Hammersley & Atkinson

Field-based document analysis is a qualitative strategy in which the researcher enters a real-world setting — a school, clinic, organisation, or community — and systematically collects, authenticates, and analyses documents that are naturally produced and used there. Unlike library-based or archival document analysis, the field context is integral: the researcher observes how documents function in practice, who produces and reads them, and what organisational or cultural work they perform. The approach is widely used in ethnographic, case-study, and institutional research.

Field-collected documents (records, artifacts, official files, written materials encountered in natural settings)

Bogdan, R. C., & Biklen, S. K. (2007). Qualitative Research for Education: An Introduction to Theories and Methods (5th ed.). Pearson.

Hammersley, M., & Atkinson, P. (2007). Ethnography: Principles in Practice (3rd ed.). Routledge.

document-analysis ethnography content-analysis case-study thematic-analysis narrative-analysis


Field-based ethnography

fieldwork ethnography
immersive ethnography
ethnographic fieldwork
site-based ethnography

Early 20th century (Malinowski 1922; Geertz 1973)

Bronislaw Malinowski; Clifford Geertz (interpretive tradition)

Field-based ethnography is a qualitative research design in which the researcher immerses themselves in a social setting or community over an extended period, observing and participating in everyday life to understand cultural practices, meanings, and social dynamics from an insider perspective. It is the classical form of ethnography, grounded in sustained physical presence at a research site, and distinguished from archival, virtual, or document-only approaches by its central reliance on direct, embodied fieldwork.

Field notes, participant observation records, interviews, artifacts, documents

Geertz, C. (1973). The Interpretation of Cultures. Basic Books.

Malinowski, B. (1922). Argonauts of the Western Pacific. Routledge.

ethnography participatory-ethnography digital-ethnography longitudinal-ethnography case-study narrative-inquiry


Field-based Grounded Theory

constructivist grounded theory
ethnographic grounded theory
situational grounded theory
field grounded theory

1967 (original GT); field-based variant developed through 1980s–2000s

Kathy Charmaz (constructivist extension); Barney Glaser & Anselm Strauss (original grounded theory)

Field-based grounded theory integrates sustained fieldwork — participant observation, field notes, and naturalistic data collection — with the iterative coding and theoretical sampling procedures of classic grounded theory. Where standard grounded theory typically relies on interview transcripts, the field-based variant anchors theory generation in direct, prolonged observation of naturally occurring social processes in context. The result is a substantive theory that is grounded in both what people say and what they actually do in their everyday settings.

Qualitative research design and analysis approach

Fieldwork observations, field notes, in-depth interviews, documents

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.

Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.

grounded-theory ethnography participant-observation thematic-analysis case-study narrative-analysis


Field-based hermeneutic phenomenology

Field-Based Hermeneutic Phenomenological Research

field hermeneutic phenomenology
naturalistic hermeneutic phenomenology
field-grounded phenomenology
van Manen field phenomenology

1990s (van Manen's field articulation); philosophical roots ~1927

Max van Manen (field application); philosophical roots in Martin Heidegger and Hans-Georg Gadamer

Field-based hermeneutic phenomenology investigates the meaning of lived experience by immersing the researcher in the natural setting where participants live, work, or act. Drawing on Heidegger's ontological hermeneutics and van Manen's pedagogical application, it combines sustained fieldwork — observation, conversation, and artefact collection — with iterative interpretive text analysis to uncover how participants understand and inhabit their world.

Field observations, in-depth interviews, field notes, artefacts, conversational narratives

van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.

Heidegger, M. (1962). Being and Time (J. Macquarrie & E. Robinson, Trans.). Harper & Row. (Original work published 1927)

hermeneutic-phenomenology interpretive-phenomenology phenomenology ethnography interpretive-phenomenological-analysis field-based-ethnography


Field-based institutional ethnography

field IE
field-based IE
institutional ethnography fieldwork
on-site institutional ethnography

1987 (IE foundations); field-based variant prominent from 1990s onward

Field-based institutional ethnography (field IE) is a qualitative approach that combines Dorothy Smith's institutional ethnography with sustained, immersive on-site fieldwork. Researchers enter real institutional settings — hospitals, schools, social service offices, prisons — to observe how everyday work practices are coordinated and governed by texts, policies, and ruling relations operating beyond the local site.

Field observations, interviews, documents, textual artifacts collected on-site

Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.

DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.

institutional-ethnography ethnography field-based-ethnography critical-institutional-ethnography participatory-institutional-ethnography field-based-grounded-theory


1999–2009 (IPA seminal; field-based adaptation emerging 2000s–2010s)

Smith, Flowers & Larkin (IPA); field extension drawn from ethnographic fieldwork traditions

Field-based Interpretive Phenomenological Analysis (Field IPA) extends standard IPA by embedding data collection within naturalistic field settings. Rather than relying solely on retrospective interviews conducted away from the site of experience, the researcher enters the actual environment — a classroom, clinic, workplace, or community space — to gather field observations, artefacts, and in-context conversations alongside in-depth interviews. This produces a richer, more situated account of how participants make sense of their lived experience in the moment and place in which it unfolds.

Field observations, fieldwork notes, in-situ interviews, artefacts

Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.

Delamont, S., & Atkinson, P. (2019). Ethnography and interpretive phenomenological analysis: Compatible or incompatible methodologies? Qualitative Research in Psychology, 16(1), 1–15.


Field-based Life History Research

life history method
biographical field research
life story research
field biography

1920s (Thomas & Znaniecki); systematised 1980s–1990s

W.I. Thomas and Florian Znaniecki (early sociological use); Robert Atkinson and Norman Denzin (methodological codification)

Field-based life history research is a qualitative design that combines sustained ethnographic fieldwork with in-depth biographical interviewing to reconstruct how individuals have experienced and given meaning to their lives within particular social, cultural, and historical contexts. Unlike archive-only biographical work, the field-based variant requires the researcher to be physically present in the participant's social world over time, gathering both spoken life stories and observational data from that world.

In-depth life story interviews, field observations, personal documents, artefacts

Atkinson, R. (1998). The Life Story Interview. Sage Publications.

Denzin, N. K. (1989). Interpretive Biography. Sage Publications.

narrative-inquiry ethnography oral-history phenomenology grounded-theory case-study


Field-based maximum variation sampling

field-based purposeful maximum variation

1990 (Patton); field application established through ecological and ethnographic practice in the 1990s–2000s

Michael Quinn Patton (maximum variation sampling); adapted for field research contexts

Field-based maximum variation sampling is a purposive strategy in which a researcher deliberately selects field sites, ecological plots, communities, or observational units that span the widest possible range of relevant characteristics. By maximising heterogeneity among selected units, the approach ensures that both common patterns shared across diverse conditions and unique features specific to particular contexts are documented, making findings robust across a broad spectrum of real-world variation.

Purposive qualitative/mixed-methods sampling strategy

Field observations, site measurements, qualitative field notes, ecological or environmental records

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Maximum variation sampling discussed in Chapter 5]

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.

maximum-variation-sampling purposive-sampling field-based-stratified-sampling field-based-cluster-sampling deviant-case-sampling typical-case-sampling


Field-based Metaphor Analysis

field metaphor elicitation
naturalistic metaphor analysis
contextual metaphor analysis
FbMA

Rooted in Lakoff & Johnson (1980); field-based application developed across educational and social science research from the 1990s onward

Field-based metaphor analysis is a qualitative method that collects and interprets spontaneous or elicited metaphors from participants in their natural settings. Grounded in Lakoff and Johnson's conceptual metaphor theory, it reveals how individuals and communities structure abstract concepts — such as teaching, leadership, or illness — through figurative language encountered or produced in real contexts. Unlike purely document-based metaphor studies, field-based variants combine data collection in natural field settings with systematic analytic coding.

Open-ended written or spoken responses, field observations, interview transcripts

Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.

Saban, A. (2009). Prospective teachers' mental images about the concept of student. Teaching and Teacher Education, 25(2), 750–764.

metaphor-analysis conceptual-metaphor-theory thematic-analysis content-analysis ethnography phenomenology


Field-based Multiple Case Study

Field-based Multiple Case Study Research

comparative case study
multi-site case study
cross-case study
multiple-case design

1984 (Yin's foundational text); 2006 (Stake's multiple-case elaboration)

A field-based multiple case study is a qualitative research design in which the researcher conducts sustained, in-person investigation at two or more bounded real-world sites (the cases), gathering data through direct observation, interviews, and document analysis. By systematically comparing what is found across cases, the researcher can identify both shared patterns and meaningful differences, producing analytic conclusions that are more robust and transferable than a single-site study allows.

Field observations, interviews, documents, artifacts gathered across multiple real-world sites

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.

case-study ethnography comparative-research phenomenology grounded-theory narrative-analysis


Field-based Multistage Sampling

multistage field sampling
field multistage probability sampling
area-based multistage sampling

Leslie Kish and William G. Cochran (survey sampling frameworks)

Field-based multistage sampling is a probability sampling approach in which the population is drawn from a geographically dispersed or operationally structured field setting through successive nested stages. At each stage, a random subset of sampling units is selected — progressing from large geographic or administrative units down to the final respondents or observation points — with field enumeration conducted between stages to update or verify the available units on the ground.

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.

Kish, L. (1965). Survey Sampling. John Wiley & Sons.

multistage-sampling cluster-sampling systematic-sampling stratified-sampling proportional-multistage-sampling field-based-cluster-sampling


Field-based narrative inquiry

field narrative inquiry
naturalistic narrative inquiry
field-situated narrative research
in-situ narrative inquiry

Field-based narrative inquiry is a qualitative research design that investigates human experience by collecting and interpreting stories directly within the natural settings where those experiences unfold. Rooted in Clandinin and Connelly's narrative inquiry framework, it moves the researcher into participants' lived worlds — classrooms, workplaces, communities — to gather rich field texts that preserve the contextual, temporal, and relational dimensions of experience through story.

Field texts: interview narratives, field notes, observations, documents, artifacts collected in naturalistic settings

Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.

Clandinin, D. J. (2013). Engaging in narrative inquiry. Left Coast Press.

narrative-inquiry ethnography field-based-ethnography field-based-case-study participatory-narrative-research longitudinal-narrative-research


Field-based netnography

hybrid netnography
field netnography
offline-online netnography
blended netnography

Robert V. Kozinets (netnography); hybrid extension developed in netnographic scholarship

Field-based netnography combines the systematic online community observation of netnography with direct in-person fieldwork. Researchers move between digital spaces and physical sites where the same community or practice exists, triangulating online discourse with face-to-face encounters. This approach is particularly suited to communities whose identity and practices span both online and offline worlds — fan communities, patient groups, activist networks, and professional subcultures, among others.

Online community data (posts, threads, multimedia) combined with in-person field observations and interviews

Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.

Kozinets, R. V. (2015). Netnography: Redefined (2nd ed.). Sage.

netnography digital-ethnography field-based-ethnography participatory-netnography virtual-ethnography field-based-digital-ethnography


Field-based Oral History

oral history fieldwork
in-situ oral history
community oral history
field oral history

1960s–1970s (modern oral history movement)

Paul Thompson; Alessandro Portelli (theoretical elaboration)

Field-based oral history is a qualitative research design in which in-depth narrative interviews are conducted on-site — at the community, location, or setting that is historically or experientially significant to participants. By situating interviews in the actual field rather than a laboratory or office, the approach activates contextual memory, enriches description, and grounds personal testimony in the material landscape it references. It is widely used in history, anthropology, sociology, and heritage studies.

Audio/video-recorded interviews, field notes, community documents

Thompson, P. (2000). The Voice of the Past: Oral History (3rd ed.). Oxford University Press.

Portelli, A. (1997). The Battle of Valle Giulia: Oral History and the Art of Dialogue. University of Wisconsin Press.

narrative-analysis ethnography life-history-research biographical-method participatory-action-research phenomenology


Field-based phenomenology

naturalistic phenomenology
field phenomenology
phenomenological fieldwork
in-situ phenomenological inquiry

1980s–1990s (van Manen's synthesis; broader tradition from early 20th century)

Max van Manen (systematic field application); rooted in Husserl and Heidegger

Field-based phenomenology is a qualitative approach that investigates the lived experience of a phenomenon by collecting data in the natural environments where that experience actually unfolds — rather than exclusively in interview rooms. Drawing on the phenomenological tradition of Husserl and Heidegger, and systematised by Max van Manen, it combines sustained fieldwork observation with open-ended, in-situ conversation to capture the experiential texture of phenomena as participants encounter them in everyday life.

Field observations, in-situ interviews, field notes, naturally occurring talk, artefacts

van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.

Finlay, L. (2011). Phenomenology for Therapists: Researching the Lived World. Wiley-Blackwell.

phenomenology ethnography interpretive-phenomenology hermeneutic-phenomenology participatory-phenomenology narrative-inquiry


Field-based program evaluation

naturalistic program evaluation
field evaluation
on-site program evaluation
field-based evaluation

1970s–1980s (field methods integration with evaluation practice)

Michael Q. Patton; Peter H. Rossi and Howard E. Freeman

Field-based program evaluation is an applied research method that assesses the implementation, outcomes, and value of a program by collecting data directly in the natural setting where the program operates. Rather than relying solely on administrative records or remote surveys, evaluators embed themselves in the field — observing activities, interviewing stakeholders on-site, and reviewing context-specific documents — to produce evidence-grounded judgments about program merit and worth.

Mixed: observations, interviews, documents, performance data collected on-site

Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage Publications.

Patton, M. Q. (2011). Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use. Guilford Press.

program-evaluation educational-action-research classroom-observation participatory-program-evaluation case-study ethnography


Field-Based Qualitative Content Analysis

naturalistic qualitative content analysis

Philipp Mayring (qualitative content analysis); applied to field settings via ethnographic and naturalistic inquiry traditions

Field-based qualitative content analysis (field QCA) combines systematic, category-driven content analysis with data collected directly in naturalistic settings. Rather than working with pre-existing texts or archived material, the researcher gathers documents, field notes, artifacts, and informal textual records during fieldwork and subjects them to rigorous qualitative content analysis. The approach preserves the contextual depth of field inquiry while applying the structured, transparent analytic logic that distinguishes qualitative content analysis from purely impressionistic reading.

Field-collected texts: field notes, observed documents, artifacts, informal interviews, on-site written materials

Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20.

Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.

qualitative-content-analysis field-based-thematic-analysis ethnographic-content-analysis field-based-discourse-analysis field-based-document-analysis thematic-analysis


Field-based Reflexive Thematic Analysis

ethnographic reflexive thematic analysis

2019–2021 (RTA formalised); field application concurrent

Virginia Braun & Victoria Clarke (RTA foundation); applied to field settings via ethnographic traditions

Field-based Reflexive Thematic Analysis (field RTA) integrates ethnographic data collection — participant observation, field notes, and naturalistic interviews — with the epistemologically explicit, researcher-centred analytic framework of Braun and Clarke's Reflexive Thematic Analysis. It is used when themes must be grounded in observed social practice rather than retrospective accounts alone, placing the researcher's active, documented reflexivity at the centre of both data gathering and interpretation.

Field notes, observation logs, interview transcripts collected in naturalistic settings

Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. Sage.

Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.

reflexive-thematic-analysis ethnography thematic-analysis grounded-theory narrative-analysis interpretive-phenomenological-analysis


Field-based Semiotic Analysis

semiotic fieldwork
ethnographic semiotics
field semiotics
social semiotics in the field

1980s–1990s (systematic field application)

Developed from Ferdinand de Saussure's semiology and Charles S. Peirce's semiotics; applied to fieldwork by Hodge & Kress (social semiotics) and later multimodal theorists

Field-based semiotic analysis is a qualitative approach that combines sustained fieldwork observation with systematic semiotic analysis of signs, symbols, and meaning-making practices encountered in a natural setting. Drawing on the social semiotic tradition of Hodge and Kress, the researcher enters a social field, records its multimodal sign systems — including visual, spatial, gestural, and textual elements — and interprets how participants use and negotiate signs to construct social meanings.

Field observations, visual artifacts, texts, spatial arrangements, embodied interactions

Hodge, R., & Kress, G. (1988). Social Semiotics. Polity Press.

van Leeuwen, T. (2005). Introducing Social Semiotics. Routledge.

ethnography discourse-analysis multimodal-analysis visual-methods content-analysis phenomenology


Field-based single case study

single-site case study
holistic single case study
naturalistic single case study
field case study

A field-based single case study is a qualitative research design that investigates one bounded real-world case — an individual, program, organization, event, or community — in its natural setting through sustained first-hand fieldwork. Drawing on Robert Yin's systematic case study logic and Robert Stake's interpretive tradition, this design combines multiple data sources collected on-site to build a rich, contextualized account of a phenomenon that cannot be separated from its real-world environment.

Field observations, interviews, documents, artifacts collected in situ

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications.

Stake, R. E. (1995). The Art of Case Study Research. Sage Publications.

case-study field-based-multiple-case-study field-based-ethnography single-case-study field-based-narrative-inquiry longitudinal-single-case-study


Field-based Snowball Sampling

in-person snowball sampling
fieldwork chain-referral sampling
field snowball sampling
face-to-face referral sampling

1961 (foundational); field variant developed through 1970s–1980s ethnographic and hidden population research

Leo A. Goodman (snowball sampling); field adaptation through ethnographic and social network research traditions

Field-based snowball sampling is a non-probability chain-referral technique in which an initial set of in-person contacts (seeds) recruit further participants from within their real-world social networks, expanding the sample iteratively through face-to-face interaction in naturalistic field settings. It is the default snowball approach in ethnographic and community fieldwork, and is particularly valuable when the target population is hidden, hard-to-reach, or lacks a sampling frame.

Categorical or continuous participant-level data gathered through in-person field contact

Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170.

Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10(2), 141–163.

snowball-sampling purposive-sampling convenience-sampling respondent-driven-sampling theoretical-sampling field-based-purposive-sampling


Field-based Stratified Sampling

field stratified sampling
stratified field survey sampling
in-field stratified sampling
field survey stratification

1934 (Neyman's stratified sampling theory); field applications throughout 20th century

Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice

Field-based stratified sampling divides a geographically dispersed or heterogeneous target population into internally homogeneous subgroups (strata) defined by features observable in the field — such as land use type, habitat zone, administrative district, or community category — and then independently draws random samples from each stratum during on-site data collection. The approach combines the precision gains of stratification with the logistical realities of fieldwork, ensuring that every identifiable subgroup of the landscape or community is represented in the final data set.

Field-collected quantitative or structured qualitative data; survey, ecological, agricultural, or epidemiological records

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). John Wiley & Sons.

stratified-sampling cluster-sampling multistage-sampling systematic-sampling proportional-stratified-sampling field-based-cluster-sampling


Field-based Straussian Grounded Theory

Straussian GT with fieldwork
fieldwork-grounded theory
Strauss-Corbin grounded theory
constructivist Straussian GT

Field-based Straussian grounded theory applies the systematic coding procedures of Strauss and Corbin's grounded theory tradition to data generated through sustained fieldwork — direct observation, ethnographic notes, informal conversations, and artefact collection — rather than relying solely on formal interviews. The goal is to generate a substantive theory that is firmly anchored in the natural social setting where the phenomenon occurs, capturing both interaction and context.

Field observations, ethnographic notes, interviews, documents

Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.

Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.

grounded-theory glaserian-grounded-theory constructivist-grounded-theory ethnography constant-comparative-method situational-analysis


Field-based systematic sampling

systematic field sampling
grid-based field sampling
regular interval field sampling

1940s–1950s (systematic sampling foundations); field adaptations consolidated by 1970s

William G. Cochran (systematic sampling foundations); adapted to field contexts in ecological and agricultural survey literature

Field-based systematic sampling applies systematic (regular-interval) selection to real-world field environments — plots of land, transects, geographic grids, or physical survey routes. A random starting point is chosen, then every k-th unit or location is sampled at equal spatial or sequential intervals. Widely used in ecology, agriculture, environmental science, and field surveys, it delivers spatially even coverage at low operational cost while maintaining probability-sampling properties.

Spatial, ecological, agricultural, or field survey observations

Thompson, S. K. (2002). Sampling (2nd ed.). John Wiley & Sons.

systematic-sampling cluster-sampling stratified-sampling multistage-sampling simple-random-sampling adaptive-cluster-sampling


Field-based theoretical sampling

field theoretical sampling
in-situ theoretical sampling
fieldwork-driven theoretical sampling
grounded field sampling

Field-based theoretical sampling is an iterative qualitative sampling strategy in which decisions about whom to observe or interview next are made during active fieldwork, guided by emerging theoretical insights from the data already collected. Rooted in Glaser and Strauss's grounded theory, it extends theoretical sampling into naturalistic, in-situ field settings — ethnographic sites, clinical environments, organizational contexts — where data collection and analysis proceed simultaneously.

Fieldwork observations, in-situ interviews, field notes, ethnographic data

Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.

theoretical-sampling purposive-sampling snowball-sampling adaptive-theoretical-sampling maximum-variation-sampling field-based-purposive-sampling


Field-based typical case sampling

field typical case sampling
in-person typical case sampling
fieldwork typical case selection
on-site typical case sampling

Field-based typical case sampling is a purposive qualitative strategy in which the researcher selects and studies cases that represent the ordinary, average, or most common instance of a phenomenon — and conducts data collection through direct fieldwork such as in-person observation, site visits, and face-to-face interviews. The combination ensures that findings portray what the phenomenon looks like under real-world, everyday conditions rather than through self-reports or online proxies.

Field observations, in-person interviews, ethnographic data, site-based documents

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.

Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis: An Expanded Sourcebook (2nd ed.). Sage Publications.

typical-case-sampling purposive-sampling field-based-purposive-sampling field-based-maximum-variation-sampling field-based-deviant-case-sampling maximum-variation-sampling


Field-based visual analysis

fieldwork visual analysis
in-situ visual analysis
ethnographic visual analysis
field visual research

1990s–2000s (systematic field-based visual methods codified)

Gillian Rose; Marcus Banks; John Collier Jr. (photo-elicitation precursors)

Field-based visual analysis is a qualitative approach in which researchers collect and analyze visual materials — photographs, video, diagrams, environmental signs, and spatial arrangements — directly within the natural settings where they are produced and used. By anchoring visual analysis in fieldwork, this method captures images and visual phenomena in their social and spatial context, enabling interpretation that goes beyond what can be achieved from decontextualized images alone.

Photographs, video recordings, maps, drawings, and other visual materials collected in natural settings

Rose, G. (2012). Visual Methodologies: An Introduction to Researching with Visual Materials (3rd ed.). Sage.

Banks, M. (2001). Visual Methods in Social Research. Sage.

visual-analysis ethnography field-based-ethnography visual-elicitation-visual-analysis field-based-document-analysis multimodal-analysis


Field Experiment

field trial
natural field experiment
randomized field experiment
field RCT

1920s–1930s (agriculture); 1990s–2000s (social sciences)

Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004)

A field experiment applies the logic of a randomized controlled trial in a naturally occurring, real-world environment rather than an artificial laboratory. Participants are randomly assigned to treatment and control conditions while going about everyday activities, allowing researchers to estimate causal effects with high internal validity while preserving a level of ecological realism that laboratory settings cannot offer. The design is especially prominent in economics, public health, political science, and development research.

Quantitative outcome measures collected in real-world settings

Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.

Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. W. W. Norton.

laboratory-experiment natural-experiment randomized-controlled-trial quasi-experiment factorial-experiment cluster-randomized-controlled-trial


Field-mapping Meta-ethnography

1988 (meta-ethnography); field-mapping application 2000s–2010s

Noblit & Hare (meta-ethnography base); field-mapping frame developed in review methodology literature

Field-mapping meta-ethnography combines the breadth of a field-mapping (scoping) review with the interpretive synthesis power of meta-ethnography. It first maps the full landscape of qualitative studies on a topic to understand what has been studied and how, then applies Noblit and Hare's seven-step meta-ethnographic synthesis to generate second-order and third-order constructs that represent the accumulated qualitative evidence across that field.

Qualitative evidence synthesis with field-mapping scope

Published qualitative research articles and reports

Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing Qualitative Studies. Sage.

Archer, M. M., Graham-Matheson, L., & Gerber, P. J. (2017). Field-mapping literature reviews and systematic approaches in education research. Review of Education, 5(2), 138–177.

meta-ethnography scoping-review mapping-review qualitative-meta-synthesis systematic-literature-review thematic-evolution-analysis


Field-mapping Scientometric Analysis

science field mapping
research field delineation
scientometric field analysis
knowledge domain mapping

2000s (mature form); roots in 1960s-1970s scientometrics

Kevin Boyack, Richard Klavans, Katy Borner (field-level science mapping); broader tradition rooted in Derek de Solla Price and Henry Small

Field-mapping scientometric analysis uses quantitative bibliometric techniques — co-citation, bibliographic coupling, co-authorship, and keyword co-occurrence — to delineate the intellectual structure and boundaries of a scientific field. By transforming large publication datasets into similarity networks and clustering them into research fronts and knowledge bases, it produces visual maps that reveal how subfields relate, where boundaries lie, and how the field evolves over time.

Publication metadata, citation counts, co-citation matrices, bibliographic coupling matrices

Boyack, K. W., Klavans, R., & Borner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351-374.

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.

scientometric-analysis bibliometric-analysis co-citation-analysis bibliographic-coupling science-mapping co-word-analysis


Field-mapping Scoping review

field-mapping scoping study
evidence-mapping scoping review
field map review
scoping review for field mapping

2005 (foundational framework); field-mapping purpose formalised c. 2015–2018

Arksey & O'Malley (scoping review framework); field-mapping purpose formalised by Munn et al. and Peters et al.

A field-mapping scoping review is a purposive variant of the scoping review in which the overarching goal is to chart the conceptual and empirical landscape of a research field — identifying what has been studied, by whom, using which methods, and where knowledge gaps remain. It follows the Arksey and O'Malley scoping framework but is explicitly oriented toward producing a structured map of a field rather than answering a focused clinical or policy question.

Evidence synthesis — systematic review variant

Published literature records (titles, abstracts, full texts)

Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic review and scoping review approach. BMC Medical Research Methodology, 18, 143.

Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.

scoping-review mapping-review systematic-literature-review bibliometric-analysis science-mapping thematic-evolution-analysis


Field Notes

fieldnotes
observational notes
ethnographic notes
jottings

Late 19th century (formalized in 20th century)

Rooted in 19th-century anthropology and sociology; systematized by ethnographers such as Bronislaw Malinowski and later Robert Emerson et al.

Field notes are detailed written records created by researchers during or immediately after direct observation in a naturalistic setting. They capture what is seen, heard, and experienced — including behaviors, interactions, physical environments, and the researcher's own analytic impressions — forming the primary data source for ethnographic and observational studies.

Qualitative data collection and recording technique

Written textual records of observations, interactions, and reflections

Emerson, R. M., Fretz, R. I., & Shaw, L. L. (1995). Writing Ethnographic Fieldnotes. University of Chicago Press.

Lofland, J., Snow, D., Anderson, L., & Lofland, L. H. (2006). Analyzing Social Settings: A Guide to Qualitative Observation and Analysis (4th ed.). Wadsworth.

participant-observation non-participant-observation ethnography diary-method research-diary case-study


Field-Oriented Control

Field-Oriented Control (FOC), also known as Vector Control, is an advanced method for controlling AC induction and permanent magnet motors by decomposing phase currents into torque and flux components and independently regulating them using PI controllers. Pioneered by Blaschke in 1972, FOC enables smooth precise motor control equivalent to DC motor performance, making it the standard for high-performance industrial variable-speed drives.

Blaschke, F. (1972). The principle of field orientation as applied to the new transvector closed-loop control system for rotating field machines. Siemens Review, 34(5), 217-220.

Hasse, K. (1969). Zur Dynamik drehzahlgeregelter Antriebe mit stromrichtergespeisten Asynchronmaschinen. ETZ-A, 90, 77-81.


Figure and Table Reporting

Standards for Presenting Data in Figures and Tables

data visualization
table design
figure captions

Tufte (visual communication theory), ICMJE standards, APA style guide

Tables and figures are the primary means of presenting research data in scientific manuscripts. A well-designed table or figure enables readers to grasp complex data patterns instantly; a poorly designed one obscures findings or misleads. The ICMJE Recommendations and APA Publication Manual establish standards for table and figure formatting, captions, legends, and referencing. Tables are best used for precise numerical values and comparisons across rows and columns; figures (graphs, plots, images) are better for illustrating trends, relationships, or distributions. Both must be self-contained (understandable without consulting the text) and referenced clearly in the manuscript.

American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). Washington, DC: American Psychological Association.

Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire, CT: Graphics Press.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.

imrad-structure statistical-reporting-standards scientific-writing-clarity apa-style-guide


Film Narrative Analysis

Film Narrative Structure and Storytelling Analysis

narrative structure analysis
story analysis in cinema

Film Narrative Analysis is a qualitative method for examining how stories are told through cinematic techniques and structures. Developed from literary narratology and adapted for film studies by scholars like David Bordwell and Mieke Bal, it deconstructs the relationship between story (fabula), plot (sjuzhet), and narration to understand how meaning is created. This method is fundamental to film criticism and provides a systematic framework for analyzing how viewers construct narrative coherence from visual and audio elements.

Analytical pipeline for deconstructing cinematic narrative structure

Bal, M. (1997). Narratology: Introduction to the Theory of Narrative. University of Toronto Press.

Bordwell, D. (1985). Narration in the Fiction Film. University of Wisconsin Press.

Thompson, K. (1999). Storytelling in the New Hollywood: Understanding Classical Narrative Technique. Harvard University Press.

Seger, L. (1987). The Art of Adaptation: Turning Fact and Fiction Into Film. Henry Holt and Company.

semiotics-film auteur-theory-analysis genre-analysis-film discourse-analysis-media reception-analysis


FiLM

FiLM (Frequency Improved Legendre Memory Model)

Frequency Improved Legendre Memory
FiLM Forecaster
Legendre Frequency Model
Frekans Tabanlı Legendre Bellek Modeli

FiLM is a long-term time-series forecasting architecture introduced by Tian Zhou and colleagues at NeurIPS 2022. It combines Legendre polynomial projections of the historical input with learnable frequency-domain filters applied to the resulting coefficient sequences. By representing history as a compact set of polynomial coefficients and filtering those coefficients in the frequency domain, FiLM enables efficient extrapolation over long prediction horizons without the quadratic cost of full self-attention.

Frequency-domain time-series forecasting model

Zhou, T., Ma, Z., Wen, Q., Sun, L., Yao, T., Yin, W., & Jin, R. (2022). FiLM: Frequency improved Legendre memory model for long-term time series forecasting. NeurIPS.


Functional Independence Measure Scale

The Functional Independence Measure (FIM) is a comprehensive 18-item scale assessing functional independence and burden of care in patients with disability across motor and cognitive domains. Developed by Granger and colleagues in 1987, FIM has become the standard outcome measure in rehabilitation medicine, mandated by Medicare for documenting rehabilitation outcomes and discharge planning in inpatient rehabilitation facilities.

Comprehensive functional independence scale

Granger, C. V., Deutsch, A., & Linn, R. T. (1998). Advances in functional assessment for medical rehabilitation. Topics in Stroke Rehabilitation, 5(2), 27–35.

Hamilton, B. B., Laughlin, J. A., Fiedler, R. C., & Granger, C. V. (1994). Interrater reliability of the 7-level Functional Independence Measure (FIM). Scandinavian Journal of Rehabilitation Medicine, 26(3), 115–119.

barthel-adl-index fugl-meyer-assessment wimberley-functional-rating modified-rankin-scale


Fine-Gray Competing Risks Model

Fine-Gray Proportional Subdistribution Hazards Model

competing risks regression
subdistribution hazard model
Fine-Gray model
Fine-Gray Competing Risks Modeli

The Fine-Gray model is a semiparametric regression method for survival data in which two or more mutually exclusive event types compete to occur first. Proposed by Fine and Gray in 1999, it models the subdistribution hazard of each event type directly, allowing covariates to be linked to the cumulative incidence function (CIF) — the quantity that actually answers 'what is the probability of experiencing event type k by time t?'. It corrects the well-known shortcoming of standard Cox regression, which ignores competing events and thereby overestimates cause-specific probabilities.

Subdistribution (cause-specific cumulative incidence)

Fine, J.P. & Gray, R.J. (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association, 94(446), 496–509.

Austin, P.C. et al. (2016). Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation, 133(6), 601–609.

kaplan-meier cox-proportional-hazards time-dependent-cox flexible-parametric-survival log-rank-test


Fine-Tuned BERT-based Classification

Fine-Tuned BERT-based Text Classification

BERT fine-tuning
BERT classifier
fine-tuned BERT
BERT sequence classification

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)

Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.

Pre-trained transformer fine-tuned for classification

Text (labelled sentence or document pairs)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.

Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Proceedings of CCL 2019, LNCS 11856, 194–206.

bert-based-classification roberta-based-classification fine-tuned-roberta-based-classification transformer fine-tuned-transformer sentence-embeddings


Fine-Tuned Convolutional Neural Network

Fine-Tuned Convolutional Neural Network (CNN Fine-Tuning via Transfer Learning)

Fine-tuned CNN
CNN fine-tuning
CNN transfer learning with fine-tuning
adapted convolutional network

Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward

Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.

Transfer learning technique (supervised fine-tuning)

Images; also video frames, spectrograms, or other 2-D grid inputs

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.

Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312.


Fine-Tuned Diffusion Model

Fine-Tuned Denoising Diffusion Probabilistic Model

DDPM fine-tuning
diffusion model adaptation
personalized diffusion model
subject-driven diffusion fine-tuning

Ho, J., Jain, A., Abbeel, P. (base DDPM); Ruiz et al. (DreamBooth fine-tuning paradigm)

A fine-tuned diffusion model adapts a large pretrained denoising diffusion model — such as Stable Diffusion or DALL-E — to a specific subject, style, or domain by continuing training on a small curated dataset. Techniques such as DreamBooth, textual inversion, and LoRA make this adaptation feasible on consumer hardware while preserving general generative capability.

Generative model (fine-tuned via subject-specific or domain-specific data)

Images, image-text pairs, domain-specific visual corpora

Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22500–22510.

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.


Fine-Tuned Doc2Vec

Fine-Tuned Doc2Vec (Domain-Adapted Paragraph Vector)

fine-tuned Paragraph Vector
domain-adapted Doc2Vec
PV fine-tuning
Doc2Vec transfer learning

2014 (base); fine-tuning practice ca. 2015

Le, Q. V. & Mikolov, T. (Doc2Vec base); fine-tuning practice adopted by the NLP community ca. 2015–2017

Fine-Tuned Doc2Vec adapts a pre-trained Paragraph Vector (Doc2Vec) model by continuing its training on a target corpus, producing document embeddings that capture both the general language knowledge of the original training and the vocabulary and style of the new domain. It is used for text classification, semantic similarity, and clustering when labeled data are scarce but unlabeled domain text is available.

Representation learning / transfer learning

Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.


Fine-Tuned Generative Adversarial Network (Domain-Adaptive GAN via Transfer)

Fine-Tuned GAN
GAN Fine-Tuning
Domain-Adapted GAN
Transfer GAN

2014 (GAN); 2019–2020 (fine-tuning paradigm)

Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020

A Fine-Tuned GAN starts from a large pre-trained generative adversarial network and continues adversarial training on a smaller target dataset, allowing the model to synthesize high-quality samples in a new domain without training from scratch. This transfer approach dramatically reduces data and compute requirements while preserving the rich feature representations learned during pre-training.

Generative model (adversarial training + transfer)

Images, structured data, text (modality-dependent)

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27.

Mo, S., Cho, M., & Shin, J. (2020). Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs. CVPR 2020 Workshop on AI for Content Creation.

generative-adversarial-network fine-tuned-diffusion-model fine-tuned-variational-autoencoder transfer-learning-gan fine-tuned-vision-transformer fine-tuned-convolutional-neural-network


Fine-Tuned GRU

Fine-Tuned GRU
GRU Fine-Tuning
Domain-Adapted GRU
GRU Transfer Learning

2014 (GRU); fine-tuning practice established 2010s

Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature

Fine-Tuned GRU adapts a Gated Recurrent Unit network — pre-trained on a large source dataset — to a specific target task or domain by continuing training on domain-specific labeled data. This combines the sequential memory capacity of GRUs with the efficiency gains of transfer learning, achieving strong performance even when labeled target data is scarce.

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724-1734.

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.

gated-recurrent-unit long-short-term-memory fine-tuned-lstm recurrent-neural-network fine-tuned-transformer transfer-learning-with-gru


Fine-Tuned Image Classification

Fine-Tuned Deep Neural Network for Image Classification

fine-tuning for image recognition
transfer learning image classifier
pretrained CNN fine-tuning
domain-specific image classifier

Yosinski, J. et al.; Pan, S. J. & Yang, Q.

Fine-tuned image classification adapts a large neural network pretrained on a broad image corpus (such as ImageNet) to a specific target domain by continuing training on labeled domain images. This approach achieves strong accuracy with far fewer target-domain samples than training from scratch, making it the dominant paradigm for applied computer vision tasks.

Image data (labeled target-domain images)

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.


Fine-Tuned LDA Topic Model

Fine-Tuned Latent Dirichlet Allocation Topic Model

Domain-Adapted LDA
Adapted LDA
LDA Fine-Tuning
Online LDA Fine-Tuning

Blei, D. M., Ng, A. Y., & Jordan, M. I. (base LDA); domain adaptation via online/warm-start LDA

Fine-Tuned LDA adapts a Latent Dirichlet Allocation model trained on a large general corpus to a specific target domain by continuing inference on domain-specific documents. Rather than fitting LDA from scratch, the pre-trained topic-word distributions are used as an informed starting point, enabling the model to discover coherent domain topics faster and with less data than training cold.

Probabilistic generative topic model (fine-tuned / domain-adapted)

Text corpora (documents as bag-of-words)

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Hoffman, M., Bach, F. R., & Blei, D. M. (2010). Online Learning for Latent Dirichlet Allocation. Advances in Neural Information Processing Systems (NIPS), 23, 856–864.

lda-topic-model nmf-topic-model fine-tuned-nmf-topic-model topic-modeling fine-tuned-bert-based-classification sentence-embeddings


Fine-Tuned LSTM

Fine-Tuned Long Short-Term Memory Network

Fine-Tuned LSTM
LSTM Fine-Tuning
Pre-trained LSTM with Task Adaptation
LSTM Transfer Learning

2018 (fine-tuning paradigm formalised); LSTM core: 1997

Howard, J. & Ruder, S. (ULMFiT); foundational LSTM by Hochreiter & Schmidhuber

Fine-Tuned LSTM adapts a Long Short-Term Memory network pre-trained on a large corpus to a specific downstream task — such as text classification, sentiment analysis, or sequence labeling — by continuing training on task-specific labeled data. Popularised by the ULMFiT framework, this approach achieves strong performance even when labeled data is scarce.

Supervised sequential model with transfer learning

Sequential / temporal data; text sequences; time series

Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339.

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.

long-short-term-memory fine-tuned-gru fine-tuned-recurrent-neural-network fine-tuned-transformer bert-based-classification transfer-learning-with-lstm


Fine-Tuned Multilayer Perceptron

Fine-Tuned Multilayer Perceptron (Transfer Learning via MLP Weight Adaptation)

fine-tuned MLP
adapted MLP
domain-adapted multilayer perceptron
MLP fine-tuning

1986 (MLP); fine-tuning practice formalised c. 2014

Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis)

A Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce.

Supervised deep learning with pre-trained weight initialisation

Tabular, text embeddings, or structured numeric/categorical features

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27, 3320–3328.


Fine-Tuned Named Entity Recognition

Fine-Tuned Named Entity Recognition (Pre-trained Language Model NER)

Fine-tuned NER
BERT NER
transfer learning NER
neural NER with fine-tuning

Devlin, J. et al. (BERT fine-tuning paradigm); Lample, G. et al. (neural NER foundations)

Fine-Tuned Named Entity Recognition adapts a pre-trained language model — most commonly BERT or one of its derivatives — to the task of identifying and classifying named entities (persons, organizations, locations, dates, etc.) in text. By fine-tuning on a relatively small labeled corpus, practitioners achieve state-of-the-art sequence-labeling performance without training a model from scratch.

Supervised token classification via fine-tuned language model

Labeled text sequences with entity span annotations (e.g., CoNLL format)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition. Proceedings of NAACL-HLT 2016, 260–270.

bert-based-classification roberta-based-classification fine-tuned-bert-based-classification fine-tuned-text-summarization fine-tuned-sentiment-analysis transformer


Fine-Tuned Question Answering

Fine-Tuned Pre-trained Language Model for Question Answering

fine-tuned QA
neural QA with fine-tuning
extractive QA fine-tuning
reading comprehension fine-tuning

Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)

Fine-Tuned Question Answering adapts a large pre-trained language model — such as BERT, RoBERTa, or a GPT-family model — to answer natural-language questions over a given context passage or knowledge base. The model learns to locate answer spans or generate free-form answers by continuing training on labeled QA pairs after general-purpose pre-training.

Transfer learning / fine-tuning for extractive or generative QA

Text (context passages, questions, answer spans or free-form answers)

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proceedings of EMNLP 2016, 2383–2392.

bert-based-classification roberta-based-classification transformer fine-tuned-bert-based-classification fine-tuned-text-summarization sentence-embeddings


Fine-Tuned Recurrent Neural Network

Fine-Tuned Recurrent Neural Network (Transfer Learning for Sequence Models)

pre-trained RNN with downstream adaptation

Popularised by Howard & Ruder (ULMFiT, 2018); RNN fine-tuning concept developed iteratively in the NLP community from ~2015

A Fine-Tuned Recurrent Neural Network (RNN) starts from a model pre-trained on large corpora or time-series data and adapts its weights to a specific downstream task through controlled gradient updates. The approach dramatically cuts the labeled data needed for strong sequence modeling performance in text classification, named entity recognition, sentiment analysis, and related tasks.

Transfer learning / sequential model adaptation

Sequential data (text, time series, speech, sensor streams)

Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of ACL 2018, 328–339.


Fine-Tuned Reinforcement Learning

Fine-Tuned Reinforcement Learning (Policy Adaptation via Fine-Tuning)

reinforcement learning from human feedback

Christiano, P. et al.; Ouyang, L. et al.

Fine-Tuned Reinforcement Learning adapts a pre-trained policy or model to a new task or behavioral objective using reinforcement signals — including human feedback — rather than retraining from scratch. Popularized by RLHF, it is the core technique behind aligning large language models and adapting deep RL agents to specialized environments with minimal additional data.

Sequential decision-making data, human feedback, reward signals

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.

Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30.


Fine-Tuned RoBERTa-based Classification

Fine-Tuned RoBERTa-based Text Classification

RoBERTa fine-tuning
RoBERTa classifier
fine-tuned RoBERTa
RoBERTa sequence classification

Liu, Y. et al. (Meta AI / University of Washington)

Fine-tuned RoBERTa-based classification adapts the RoBERTa pretrained transformer — itself a robustly retrained variant of BERT — to a specific text classification task by appending a classification head and continuing training on labeled examples. It consistently achieves state-of-the-art or near-state-of-the-art performance on sentiment analysis, topic classification, toxicity detection, and similar NLP tasks.

Pretrained transformer fine-tuned for classification

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692.

bert-based-classification roberta-based-classification fine-tuned-bert-based-classification transformer fine-tuned-transformer sentence-embeddings


Fine-Tuned Semantic Segmentation

Fine-Tuned Semantic Segmentation (Transfer Learning for Dense Pixel-wise Classification)

fine-tuned semseg
domain-adapted semantic segmentation
transfer learning semantic segmentation
pretrained dense prediction fine-tuning

Long, Shelhamer & Darrell (FCN); Chen et al. (DeepLab)

Fine-tuned semantic segmentation adapts a deep neural network pre-trained on a large pixel-labelled dataset (e.g., ImageNet-pretrained backbone with an encoder-decoder head trained on COCO or Cityscapes) to a new target domain by continuing training on domain-specific annotated images. The result is a model that assigns a class label to every pixel in an image while leveraging rich visual representations learned from vastly more data than the target domain alone could provide.

Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.


Fine-Tuned Sentence Embeddings

Fine-Tuned Sentence Embeddings (Domain-Adapted Sentence Representation Learning)

SBERT fine-tuning
sentence transformer fine-tuning
domain-adapted sentence embeddings
fine-tuned sentence encoders

Fine-Tuned Sentence Embeddings adapt a general-purpose pre-trained sentence encoder — such as Sentence-BERT — to a specific domain or task by continuing training on labeled or paired text data from that domain. The resulting embeddings capture domain-specific semantic structure far better than off-the-shelf vectors, improving downstream tasks such as semantic similarity, clustering, classification, and retrieval.

Supervised / contrastive fine-tuning of pre-trained sentence encoders

Text (sentence pairs or labeled text corpora)

Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992.

Reimers, N., & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4512–4525.

sentence-embeddings bert-based-classification roberta-based-classification fine-tuned-bert-based-classification transformer fine-tuned-transformer


Fine-Tuned Text Summarization

Fine-Tuned Pre-trained Sequence-to-Sequence Model for Text Summarization

Abstractive summarization via fine-tuning

Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)

Fine-Tuned Text Summarization adapts a large pre-trained sequence-to-sequence model — such as BART, T5, or PEGASUS — to generate concise summaries of documents by training on domain-specific (document, summary) pairs. The approach yields substantially more fluent and faithful summaries than extractive or generic approaches by leveraging knowledge encoded in billions of pre-training tokens.

Fine-tuned sequence-to-sequence neural model

Text corpora with (document, summary) pairs

Zhang, J., Zhao, Y., Saleh, M., & Liu, P. J. (2020). PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 11328–11339.

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 7871–7880.

transformer bert-based-classification fine-tuned-bert-based-classification fine-tuned-question-answering sentence-embeddings roberta-based-classification


Fine-Tuned Topic Modeling

Fine-Tuned Neural Topic Modeling with Pre-trained Language Models

neural topic modeling
fine-tuned topic model
pre-trained topic model
contextual topic modeling

Fine-Tuned Topic Modeling adapts pre-trained language models — such as BERT or Sentence-BERT — to discover latent topics in document collections. Unlike classical probabilistic methods (LDA, NMF), it leverages rich contextual embeddings and optionally fine-tunes the backbone on domain-specific corpora, producing more coherent and semantically meaningful topics, especially on short texts or specialized domains.

Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). Cross-lingual Contextualized Topic Models with Zero-shot Learning. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 1676–1683.

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.

lda-topic-model nmf-topic-model bert-based-classification sentence-embeddings topic-modeling fine-tuned-bert-based-classification


Fine-Tuned Transformer

Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models)

Transformer fine-tuning
pre-trained transformer fine-tuning
task-adaptive transformer
downstream-tuned transformer

Vaswani et al. (architecture); fine-tuning paradigm popularised by Howard & Ruder, Devlin et al.

Fine-tuning a Transformer adapts a large pre-trained model — such as BERT, GPT, or ViT — to a specific downstream task by continuing gradient-based training on a labelled target dataset. This two-stage paradigm (pre-train then fine-tune) consistently achieves state-of-the-art results across NLP and computer vision tasks with far less task-specific data than training from scratch.

Transfer learning / supervised fine-tuning

Text, images, audio, or structured sequences

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171–4186.

transformer bert-based-classification roberta-based-classification fine-tuned-bert-based-classification transfer-learning-with-transformer fine-tuned-recurrent-neural-network


Fine-Tuned Variational Autoencoder

Fine-Tuned Variational Autoencoder (Domain-Adapted VAE)

fine-tuned VAE
domain-adapted VAE
transfer-learned VAE
adapted variational autoencoder

2014 (VAE); fine-tuning practice from 2015 onward

Kingma, D. P. & Welling, M. (VAE); fine-tuning strategy from transfer learning literature

A Fine-Tuned Variational Autoencoder begins with a VAE pre-trained on a large source dataset and then continues training on a smaller target-domain dataset. This approach adapts the learned latent representation and generative capacity to new data, preserving general structure while specializing to the target distribution — yielding better results than training from scratch when labeled or large target data is scarce.

Images, continuous tabular data, time series, biological sequences

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.


Fine-Tuned Vision Transformer

Fine-Tuned Vision Transformer (ViT with Task-Specific Adaptation)

Fine-Tuned ViT
ViT fine-tuning
Vision Transformer transfer learning
ViT downstream adaptation

Fine-Tuned Vision Transformer adapts a large pre-trained ViT model — which splits images into fixed-size patches and processes them through self-attention layers — to a new image classification or recognition task using a relatively small labeled dataset. It achieves state-of-the-art accuracy in computer vision by leveraging rich representations learned during large-scale pre-training.

Transfer learning / fine-tuning of attention-based image model

Image data (labeled downstream task dataset)

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR 2021).

Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022). Scaling Vision Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), pp. 12104-12113.


Fine-Tuned Word2Vec

Fine-Tuned Word2Vec (Domain-Adapted Word Embeddings via Continued Training)

domain-adapted Word2Vec
continued-training Word2Vec
Word2Vec fine-tuning
W2V domain adaptation

2013 (Word2Vec); fine-tuning practice 2014–2016

Mikolov, T. et al. (Word2Vec); fine-tuning practice generalised by the NLP community post-2013

Fine-Tuned Word2Vec adapts a pre-trained Word2Vec model to a specific domain or task by continuing its training on domain-specific text. Rather than training embeddings from scratch, practitioners load general-purpose vectors (e.g., Google News embeddings) and run additional Skip-gram or CBOW epochs on domain corpora, shifting word representations toward domain-specific usage patterns.

Raw text corpora (domain-specific and general)

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR 2013 Workshop.

Goldberg, Y., & Levy, O. (2014). word2vec Explained: Deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722.

sentence-embeddings bert-based-classification fine-tuned-bert-based-classification fine-tuned-sentence-embeddings lda-topic-model recurrent-neural-network


Finite-Difference Time-Domain

The Finite-Difference Time-Domain method is a computational technique for solving Maxwell's equations by discretizing space and time on a grid. Introduced by Kane Yee in 1966, FDTD is a foundational approach in computational electrodynamics and optical simulation, enabling direct modeling of electromagnetic wave propagation through complex media.

Yee, K. S. (1966). Numerical solution of initial boundary value problems involving Maxwell's equations in isotropic media. IEEE Transactions on Antennas and Propagation, 14(3), 302-307.

Taflove, A., & Hagness, S. C. (2005). Computational Electrodynamics: The Finite-Difference Time-Domain Method (3rd ed.). Artech House.

Sullivan, D. M. (2000). Electromagnetic simulation using the FDTD method. IEEE Press.


Finite Element Analysis

Finite Element Analysis (FEA) is a numerical technique for obtaining approximate solutions to boundary value problems described by differential equations. Developed systematically by Richard Courant in 1943 and popularized by Clough in the 1960s, FEA divides a complex domain into smaller, simpler elements to solve engineering problems involving stress, strain, heat transfer, and fluid flow. It is the dominant computational method in materials science for predicting material behavior under various loading conditions.

Zienkiewicz, O. C., & Taylor, R. L. (1977). The Finite Element Method in Engineering Science. McGraw-Hill.

Reddy, J. N. (2019). An Introduction to the Finite Element Method (4th ed.). McGraw-Hill Education.

Bathe, K. J. (2014). Finite Element Procedures (2nd ed.). Prentice Hall.

boundary-element-method molecular-dynamics phase-field-modeling nudged-elastic-band-method