Changing the narrative: shaping legislation to advance diversity on boards in Canada
Bibliographic record
Abstract
Purpose This paper examines actors and discourses shaping new Canadian legislation designed to advance diversity in corporate governance. Design/methodology/approach This paper performs a stakeholder and discourse analysis drawing on texts of parliamentary debates. Findings The paper illuminates tensions regarding definitions of diversity, its importance for boards of directors and the mechanisms favoured for implementation. Official discourses examined show that, unlike for other political issues, opposition was largely muted, and most stakeholders engaged in the process supported legislation advancing diversity. Nonetheless areas of debate and positioning by actors and suggest important differences, with outcomes linked to non-traditional power bases. Research limitations/implications This study provides insights into the discursive environments of organizations and processes relating to promoting diversity and equality in the political decision-making domain, a critical venue for understanding advancement of equity, often neglected in organizational studies. Practical implications By understanding the complex and competing discourses surrounding diversity and inclusion at the macro level this paper provides a context for understanding organizational (meso) and individual (micro) beliefs and behaviours. Social implications This study shows how advocacy shapes how policy and legislation are framed and the ways mainstream organizations, including women's groups, may advance gender equality without regard to other dimensions of diversity or intersectionality. Originality/value This study maps the political discourse around recent Canadian legislation designed to improve diversity on boards that must, in the Canadian context, address more than gender.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.045 | 0.025 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".