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DARWIN–JEV Risk–CAM Synthetic Replication Starter
Principal investigator: Dr. Saeid Homayoun
Target study: Do Auditors Disclose the Risks That Matter? Ex Ante Accounting Risk, Critical Audit Matter Alignment, and Subsequent Reporting Quality
Journal target: Journal of Accounting Research
Version: 2026-10-16
Public contents
This public distribution contains synthetic demonstration data, schema validation code, tests, and source metadata only. It does not contain the unpublished manuscript, private Google Drive observations, restricted third-party datasets, proprietary model weights, real CAM/ICFR labels, or empirical results.
Canonical source
- GitHub: https://github.com/Saehon/Saeid-Homayoun/tree/research/jar-risk-cam-publication-20261016/research/jev-ai-audit/jar-risk-cam
- Publication package (private Google Drive): https://drive.google.com/drive/folders/1rX5r3VIS5x4prq0hlFFNIrH6OTACyuvB
Data fields
firm_id, fiscal_year, domain, industry, cutoff_date, evidence_available_date, cam_present, eligible, risk_probability, outcome_event, outcome_observed_date, icfr_status, accounting_standard.
All rows in the public example are invented. Do not use them for substantive inference.
Validation
The included contract tests reject duplicate firm-year-domain keys, predictor look-ahead, nonfuture outcomes, invalid probabilities, and invalid schema categories.
Research status
No real-data regression, trained DARWIN-JEV model, Jev API inference, statistical significance, or FT50 empirical replication is claimed.
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