The impact of board traits on the social performance of Canadian firms
Bibliographic record
Abstract
Purpose – This paper aims to examine if certain board characteristics have an impact on the corporate social responsibility (CSR) score of corporations. Design/methodology/approach – The authors’ paper analyzes the link between the ratings of CSR of the largest publicly traded Canadian firms (i.e. those included in the S&P/TSX 60 index) and the traits of their boards. Findings – The authors’ examination concludes that the CSR score is positively linked with the percentages of women and independent directors. The study did not find a link in the cases of board characteristics, namely, director’s remuneration, director’s tenure and director’s ownership. Research limitations/implications – The study focuses on the 60 largest public Canadian firms, which are strongly scrutinized. An analysis that includes smaller firms as well may show different results. Practical implications – To improve the ability of boards of directors to deal with CSR, the appointment of women and independent directors should be given greater emphasis. Data show that all boards in their sample are composed of at least 50 per cent of independent directors, with an average of 80 per cent. Thus, there is a more limited room to ameliorate CSR by adding independent directors. In contrast, women represented, on average, only 14.25 per cent of all directors. Companies wanting to improve their CSR should consider appointing more female participation in their boards. Originality/value – The paper contributes to the extant literature on corporate governance by presenting evidence of a link between CSR and certain board characteristics.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".