Bibliographic record
Abstract
The aim of this study is to investigate the effect of board structure on earnings response coefficient (ERC) and Grade of Disclosures Information of companies accepted in Tehran Stock Exchange.The present study is an applied research in terms of its purpose.Also, a correlational research method has been used in this study.The statistical population includes all the companies accepted in Tehran Stock Exchange which have been continuously active from the beginning of 2007 to the end of 2012.117 companies listed on the Tehran Stock Exchange were selected as the statistical sample.The research results showed that there is a negative and significant relationship between the board's tenure and the independence of the board with the Grade of Disclosures Information.Also, a direct and significant relationship was discovered between the size of the board of directors and the Grade of Disclosures Information.The size of the company had a positive and significant relationship with the disclosure rate, while the ratio of book value to market value and coefficient β had a negative and significant relationship with the quality of disclosures information and also, according to the information gained, there was no significant relationship between long term debt to assets ratio and voluntary disclosure rate.Also, board tenure and director independence have a reverse and significant relationship with the earnings response coefficient, while the board size has a direct and significant relationship with the earnings response coefficient.Except β coefficient which has no significant relationship with earnings response coefficient, long term debt to assets ratio has a negative and significant relationship with earnings reaction coefficient and the size of the company and the ratio of book value to market value have a positive and significant relationship with the earnings response coefficient.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.897 | 0.919 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".