Bibliographic record
Abstract
The purpose to this study was to describe and explore the difference in the board composition and characteristics of sustainability performing companies compared with other companies in terms of gender, ethnicity, and affiliation, uniquely, the inclusion of directors from a non-business background. This exploratory study used a cross-sectional design in the form of a quantitative comparative analysis, and a longitudinal design in the form of a trend analysis to compare the differences in board composition between a sample of sustainability performing companies and a sample of other companies listed on the FTSE/JSE All Share Index between 2004 and 2010. Inclusion on the Social Responsibility Investment (SRI) Index was used as a proxy for sustainability performance. 13The study provided support that director background as a board attribute may be linked to overall sustainability performance. It further provided insight into who board members should be, namely non-executive directors with non-business backgrounds. 14The findings of this study suggest that the nomination committees of companies wanting to improve sustainability performance should consider the recruitment and appointment of non-executive directors from non-business backgrounds on to their boards. The study provides grounds for further empirical studies on the causal relationship between board compositions and sustainability performance
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".