The Public Policy Debate on Investors' Need for Disclosure Regulation: Accounting Historians' Help Wanted?*/ LE DÉBAT PUBLIC SUR LES BESOINS DES INVESTISSEURS EN MATIÈRE DE RÉGLEMENTATION DE L'INFORMATION: FAUT‐IL L'AIDE DES HISTORIENS DE LA COMPTABILITÉ?
Bibliographic record
Abstract
ABSTRACT This paper explores how research in accounting history can contribute to the important public policy debate regarding investors' need for disclosure regulation. Accounting, finance, and economics researchers and practitioners argue for, as well as against, disclosure regulation. The debate remains theoretical, however, because empirical studies are virtually nonexistent. This paper reviews five contexts in which accounting historians can begin a search for empirical insights concerning the costs, benefits, externalities, and effects on stakeholders of disclosure regulation. The paper's investigation of the accounting history literature suggests that accounting historians could improve the quality of the debate and help accommodate broader interests or alternative solutions to financial crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".