def calculate_traceability_loss(self, generated_content, source_anchors):
        """
        Implementation of Option C: Traceability Bias.
        
        Philosophy:
        "Knowledge without origin is hallucination or theft."
        The model is penalized if it generates confident facts 
        without a strong attention link to a trusted 'Source Anchor'.
        """
        # 1. Source Anchors: The 'Z-Protocol' certified data blocks (Ethical Sources)
        # 2. Attention Map: How much did the model look at these anchors?
        
        # We enforce a "Citation Constraint":
        # If the model outputs a high-entropy fact (not a common stop word),
        # it MUST have a high attention score on at least one Source Anchor.
        
        fact_confidence = self.assess_factuality(generated_content)
        source_connection = self.source_attention_layer(generated_content, source_anchors)
        
        # The "Plagiarism Penalty"
        # If confident but no source connection -> High Loss
        traceability_loss = fact_confidence * (1.0 - source_connection)
        
        return traceability_loss