Bibliographic record
Abstract
The lack of gender parity in the governance of business corporations has ignited a heated global debate, leading policymakers to wrestle with difficult questions that lie at the intersection of market activity and social identity politics. Drawing on semi-structured interviews with corporate board directors in Norway and documentary content analysis of corporate securities filings in the United States, Challenging Boardroom Homogeneity empirically investigates two distinct regulatory models designed to address diversity in the boardroom: quotas and disclosure. The author's study of the Norwegian quota model demonstrates the important role diversity can play in enhancing the quality of corporate governance, while also revealing the challenges diversity mandates pose. His analysis of the US regime shows how a disclosure model has led corporations to establish a vocabulary of 'diversity'. At the same time, the analysis highlights the downsides of affording firms too much discretion in defining that concept. This book deepens ongoing policy conversations and offers new insights into the role law can play in reshaping the gendered dynamics of corporate governance cultures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".