Empirically Investigating the Disclosure of Nonfinancial Information: A Content Study on Corporations Listed in the Saudi Capital Market
Bibliographic record
Abstract
This study empirically assesses the disclosure of nonfinancial information in corporate reporting. In examining the contents of annual and board reports for 50 listed corporations, a coding sheet was developed by combining the two coding sheets of Boshnak and the European Directive 2014/95/EU. All corporations in three sectors—energy, utilities, and materials, which collectively represents 85.51% of the Saudi market capitalization—encompass the sample. Results reveal that employees, community, and products and services information have a moderate disclosure level. In contrast, environmental, customers, and fighting corruption have a low level. The findings also show that nonfinancial disclosure of the selected sectors on average range between 28.85% for the corporations in the material sector to 39.22% for the corporations in utilities sector. The corporations in the energy sector scored, on average, 37.65%. The mean for the entire sample of the ratios of disclosed nonfinancial items is 30.35%. However, the average disclosure level is without substantial improvement since 2012 and 2013, as previously reported The Capital Market Authority (CMA) is recommended to mandate nonfinancial information disclosure. It is a step toward realization aspects of Saudi Vision 2030 concerning with, for instance, protecting environment and other related matters.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".