Combining Financial Information and Corporate Social Responsibility Related Information for Characterizing Corporate Disclosure: Some Insights From Moroccan Context
Bibliographic record
Abstract
This paper deals with the corporate disclosure and therefore the option to predict the corporate disclosure through combining financial and non-financial information. In this paper, we study the corporate disclosure characteristics by investigating the predictability strength of specific financial performance indicators and corporate social responsiblity (CSR) related information. The sample of this research contains 58 organizations that had been awarded the label of the CSR in Morocco. A content analysis of corporate websites, financial statements and annual reports are used for each organization. Based on corporate disclosure content, two groups are constructed. We use four financial indicators for measuring the performance (financial information) and particular CSR related information (non-financial information) for these two groups. The discriminant analysis highlights to what extend specific information could predict the nature corporate disclosure content. As results, these indicators and information show different levels of ability to predict corporate disclosure content. Our findings, when confronted to the literature, explicit convergences about the predictability of corporate disclosure content.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".