The effect of demographic factors among the nomination committee members on earnings management in companies listed on the Amman Stock Exchange
Bibliographic record
Abstract
This study aims to investigate the effect of demographic factors for nomination committee (NC) on earnings management (EM) in companies listed on the Amman Stock Exchange. The independent variables are gender, age, level of education, and experience, while the dependent variable is EM. The study utilizes various statistical processes through SPSS 28. The results indicate there are no statistically significant differences at the level of significance (α=0.05) in the total study “EM in the listed companies” due to the age and experience variables. In addition, there are statistically significant differences at the level of significance (α=0.05) in the total study “EM in the listed companies” due to the gender variable, in favor of males and due to the level of education. Moreover, there is a significant difference between two degrees of Diploma and master’s degree in favor of the Master's degree by a mean of 3.701, but the Diploma category mean is 3.400 and the significant difference between Diploma and Ph.D. is in favor of the Ph.D. category by mean (3.722), but the Diploma category mean is (3.400).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".