{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp07_retrospective_selector60_20260803","cell_code":"E7C031","selector_replication":3,"assessments":[{"blind_id":"CANDIDATE_A","problem_reality_importance":83,"causal_archetype_fit":84,"distinctiveness_prior_art_resilience":65,"operational_specificity":91,"falsifiability_test_quality":91,"adopter_partner_path":88,"deployability_complexity":66,"authority_safety_reversibility":94,"strict_potential":76,"empirical_partner_potential":90,"scrutiny_priority":82,"biggest_visible_risk":"Constructing and maintaining detailed expected-response models may consume the evaluation capacity the residual hierarchy is intended to save while reinforcing playbook-centered interpretations.","rationale":"The proposal addresses a consequential evaluation bottleneck with a bounded target, reconstructive residuals, cross-layer propagation, independent raw auditing, and a highly reversible retrospective study. Its strongest lane is empirical: an exercise owner can directly compare findings, reconstruction fidelity, protected-signal preservation, and total effort. Strict potential is reduced because inject-response matrices, discrepancy analysis, and after-action review make parts of the workflow look vulnerable to nearby practice, while the hierarchical machinery may add cost and confirmation bias without improving findings."},{"blind_id":"CANDIDATE_B","problem_reality_importance":70,"causal_archetype_fit":82,"distinctiveness_prior_art_resilience":55,"operational_specificity":92,"falsifiability_test_quality":94,"adopter_partner_path":92,"deployability_complexity":80,"authority_safety_reversibility":95,"strict_potential":67,"empirical_partner_potential":89,"scrutiny_priority":78,"biggest_visible_risk":"Displaying predicted prior judgments may anchor experts and manufacture apparent stability or convergence, undermining the independent reconsideration Delphi rounds are meant to elicit.","rationale":"This is exceptionally testable and partner-ready: the crossover design can measure anchoring, rationale quality, minority preservation, reconstruction, workload, and fallback under non-decision-bearing conditions. Active confirmation and explicit missingness are strong safeguards. However, the underlying burden may be modest or even purposeful, and the intervention resembles prefilled questionnaires, tracked changes, and differential encoding. Its central efficiency mechanism directly introduces a known-looking elicitation hazard, lowering strict and distinctiveness potential."},{"blind_id":"CANDIDATE_C","problem_reality_importance":89,"causal_archetype_fit":95,"distinctiveness_prior_art_resilience":86,"operational_specificity":90,"falsifiability_test_quality":88,"adopter_partner_path":85,"deployability_complexity":68,"authority_safety_reversibility":94,"strict_potential":88,"empirical_partner_potential":89,"scrutiny_priority":92,"biggest_visible_risk":"Causal attribution may be non-identifiable, allowing coincident independent evidence or criticism to be incorrectly cancelled as an organizationally generated echo.","rationale":"The proposal identifies a credible self-confirmation loop that ordinary source deduplication does not address, and the outbound-action copy is causally essential rather than decorative. The pre-action manifest, bounded echo prediction, protected bypasses, raw-source retention, independent adjudication, and separation of echo-model updates from strategy decisions create a strong strict case and a safe partner study. The decisive vulnerability—whether the model improves attribution beyond simple provenance and timing without over-cancellation—is explicitly measurable, although overlapping actions and endogenous networks may limit deployment."},{"blind_id":"CANDIDATE_D","problem_reality_importance":86,"causal_archetype_fit":89,"distinctiveness_prior_art_resilience":69,"operational_specificity":91,"falsifiability_test_quality":92,"adopter_partner_path":89,"deployability_complexity":70,"authority_safety_reversibility":95,"strict_potential":80,"empirical_partner_potential":92,"scrutiny_priority":86,"biggest_visible_risk":"The shared scenario expectation may become a self-confirming filter that suppresses weak, unfamiliar, or minority evidence precisely because it lies outside the model's representation.","rationale":"This proposal has a clear attention-budget problem, a complete operational protocol, interpretable silence through heartbeats, and an excellent historical shadow comparison capable of changing a deployment decision. Shared versioned expectations and reconstructive residuals are structurally credible for distributed scanning, with strong fallback and protected-signal controls. Its strict case is weakened by resemblance to report-by-exception, change logs, alert triage, and periodic scenario refresh, plus the possibility that weekly model maintenance and audits erase the claimed efficiency."}],"rank_order":["CANDIDATE_C","CANDIDATE_D","CANDIDATE_A","CANDIDATE_B"],"top_choice":"CANDIDATE_C","portfolio_observation":"All four proposals offer unusually bounded, reversible shadow studies, so empirical-partner potential is broadly stronger than strict potential. Candidate C stands out because its causal loop and archetype mapping are most distinctive and consequential; D and A are strong partner tests but more exposed to standard exception-reporting or discrepancy-review comparisons, while B has the cleanest experiment but the weakest problem and prior-art posture.","confidence":"HIGH"}