Board Diversity and Accounting Conservatism: Evidence from Jordan
Bibliographic record
Abstract
The main aim of this research is to investigate whether board diversity affect the accounting conservatism. This study depends on a panel data set drawn from 68 industrial firms listed on Amman Stock Exchange (ASE) for the period from 2013 to 2016. Four demographic characteristics of directors have been investigated, namely: gender diversity, education level, average age and nationality diversity. Accounting conservatism was measured by accrual-based conservatism. The results indicate that gender diversity, education level and nationality diversity are significantly positively correlated with accounting conservatism. However, the findings fail to reveal any significant effect for directors' age on accounting conservatism. The findings of this study assert that it is necessary to take board diversity (directors' demographic characteristics) into account when choosing board of directors members because demographic characteristics diversity influences directors' behavior to deal with different issues that are related to accounting principles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".