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 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 | 0.007 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".