Measuring partial membership in categories: Alternative tools

Almost any attempt at classification runs into a boundary problem. Some cases fit neatly into one category, some fit one category only partially, and some fit multiple categories. This is a well-understood issue among both cognitive psychologists, who have documented how the brain’s hard-wiring classifies stimuli, and taxonomists,1 who seek to “soft-wire” additional sorting schemes. My focus here is mostly on the soft wiring. How, exactly, should researchers build classification systems—referred to here as taxonomies—that account for partial membership in categories, if at all? An important reference point is fuzzy sets, an intriguing concept that has gained some traction in sociology and political science. I explore a set of measurement strategies for assigning partial membership scores in the context of executive-legislative relations, a research domain overdue for innovation in conceptualization and measurement.