Does Board Gender Diversity Influence SDGs Disclosure? Insight from Top 15 JSE-Listed Mining Companies
Bibliographic record
Abstract
An assessment was made halfway into the sustainable development goals (SDGs) agenda period, and the findings indicated a slower than anticipated pace towards the implementation of the SDGs agenda. One of the possible causes of the slower pace is a lack of strong governance mechanisms such as gender diversity, sustainability committees, and board sustainability experience in institutions. The study sought to investigate the influence of board gender diversity on SDGs disclosure amongst the top 15 JSE-listed mining companies in light of their contribution towards the attainment of this global agenda. Mining in South Africa affects about nine percent of the country’s population. The study was anchored on the agency and the stakeholder theories. This is quantitative research which employed a keyword search to measure SDGs disclosure in the annual integrated reports for the sampled companies from 2019 to 2023. The study hypothesised that there is a significant positive relationship between a female-dominated board and SDGs disclosure in the sampled companies. Descriptive statistics, correlation analysis, as well as regression analysis were employed. The results established a lack of significant evidence of a positive or negative relationship between gender diversity and SDGs disclosure, a significant positive relationship between board size and SDGs disclosure, and no relationship between board independence and SDGs disclosure in the sampled mining companies. It was concluded that board gender diversity in corporate boards in the top 15 JSE-listed mining companies has no impact on the SDGs disclosure. The study recommends including more moderating factors and conducting more empirical studies towards the attainment of conclusive results in this space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".