A multinational test of determinants of corporate disclosure
Bibliographic record
Abstract
This paper develops a model of cultural, national, and corporate factors that influence the financial disclosure of corporations. This model is then tested empirically using a sample of companies from 33 countries. The paper extends the literature on disclosure by considering a larger number of variables that represent determinants of disclosure and by empirically testing the model using a larger number of countries than prior studies. The model is tested using disclosure scores included in International Accounting and Auditing Trends. The model considers the influence of culture, national political and economic systems, and corporate financial and operating systems on the amount of corporate financial disclosure. The results of the regression model indicate that disclosure is influenced by culture, national systems, and corporate systems. The model developed is shown to provide a reasonably good explanation of the disclosure decision. Differences among the components of the model help explain differences in observed financial disclosure between companies in different countries and between companies within the same country. The results indicate that the financial-disclosure decision for a company is complex and influenced by many national and corporate factors.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".