Canadian Evidence of Adherence to “Comply or Explain” Corporate Governance Codes: An International Comparison
Bibliographic record
Abstract
Abstract This study documents the rate of compliance by Canadian public firms with corporate governance recommendations imposed by the Canadian Securities Administrators. Canada uses a “comply or explain” governance structure in which harmonized provincial regulation establishes mandatory disclosure of governance practices. Firms can be compliant with these requirements either by voluntarily adopting the recommended best practices (i.e., adopt) or by explaining the alternative practices implemented to achieve the same governance principle (i.e., explain). Firms that fail to comply (i.e., neither adopt nor explain) are in violation of Canadian securities regulation with respect to governance. Using a hand‐collected sample of 742 Canadian public companies and 16 governance recommendations, our results show that an average of 82 percent of firms complied by adopting the best practice and an additional 4 percent complied by explanation. Our study also shows that 39 percent of Canadian publicly traded firms were completely compliant with all 16 recommendations examined in this study, either by adoption or explanation. To provide a broader context for these results, we compare rates of compliance in Canada to rates in Australia, a country broadly similar to Canada with comparable governance recommendations. The Australian Securities Exchange supplied data sample of 1334 Australian companies reports a complete compliance rate of 74 percent compared to Canada's 39 percent complete compliance rate. Our analysis shows that compliance by adoption of best practice is more common in Canada, whereas compliance by explanation is more common in Australia. In our analysis of compliance with individual recommendations, we find that half of the recommendations are more likely to be complied with in Australia, and the other half are more likely to be complied with in Canada.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".