Women on Corporate Boards and Sustainability Reporting: A Proposed Integrated Framework of Determinants and Impacts
Bibliographic record
Abstract
ABSTRACT Recent research has increasingly focused on the representation of women on boards and their impact on sustainability reporting. Although various factors influencing the relationship between female directors and the quality of sustainability reporting have been explored, no study has integrated a comprehensive framework that addresses the primary determinants and benefits of gender diversity in this context. This study aims to identify the critical internal and external determinants that influence the effect of gender diversity on sustainability reporting, as well as the supporting theories. We conducted a systematic review of the literature of 124 empirical articles published between 2000 and 2024, suggesting a unified framework that includes 20 benefit groups, 15 internal factors and 16 external factors that affect sustainability reporting. The findings may be useful for policymakers, researchers and decision makers in understanding how to leverage the positive effects of gender diversity in sustainability reporting and identifying the factors that influence this relationship.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".