The influence of board gender diversity on the sustainable development goals reporting: evidence from Portuguese companies
Bibliographic record
Abstract
Aim: The aim of this paper is to analyze the influence of female presence on boards on the level of disclosure about the sustainable development goals (SDGs). Methodology and Sample: This study used a content analysis of the sustainability/integrated reports published by a sample of the largest Portuguese listed companies. A set of panel data regression analyses on the SDGs disclosure index from 2016 until 2020 was run. Research Findings: It was expected that there would be higher levels of SDGs-related disclosures in companies with female presence on boards. However, the results do not reveal any significant association with the dependent variable (SDG_IND). Contrary to our expectation, the presence of female on boards does not influence the disclosure about the SDGs in the largest listed companies. Theoretical/Academic Implications: Little research has addressed the influence of females on SDGs reporting (Rosati & Faria, 2019b, Pizzi et al., 2021). To the best of our knowledge, this study provides a first insight at the influence of the board gender diversity on SDGs reporting in the Portuguese context. Practitioner/Policy Implications: This study helps to highlight the importance of women on boards' role by increasing awareness about UN 2030 Agenda and ensuring the transparency of SDGs-related disclosure. Thus, our findings could have implications for policy formulation, to encourage board gender diversity and its effects on SDGs reporting quality.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".