The Effect of Firm Characteristics on the Disclosure of IAS/IFRS Information: The Cases of Tunisia, France and Canada
Bibliographic record
Abstract
The effect of firm characteristics on the disclosure of IAS/IFRS information can not be studied in isolation of the national context of the country of nationality or domicile of the firm. Starting from the assumption that the intrinsic characteristics of the firm depend significantly on its size and the country of his nationality, we chose to work on companies belonging to different trading indices and from countries with different cultures and levels of economic development. The selected countries are Tunisia, France and Canada since Tunisia differs from Canada and France mainly by the level of economic development (developing countries) and France differs from Canada by culture. Our sample includes 52 Tunisian companies (40 listed on the first market and 12 on the alternative market), 244 French companies (35 CAC40 Index (top 40 French firms) and 209 CACsmall (index of small Capitalization French firms)) and 223 Canadian companies (36 ^TX60 (first 60 Canadian companies) and 187 ^TX20 Index (Small Capitalization Canadian firms)). Our results showed that the determinants of the disclosure of IAS/IFRS information will vary depending on the nationality of the firm and also showed the importance of the nationality of the firm in explaining disclosed information since the proxy used "country" has significant coefficients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".