Cultural differences and board gender diversity
Bibliographic record
Abstract
As evidence of the continuing interest raised by "board gender diversity", major studies (Catalyst, 2008; World Economic Forum, 2010; European Board Diversity Analysis, 2010) were recently carried out and have all led to reports confirming the imbalance of women on boards and the need to address this issue. Moreover, our analysis of these reports indicates that the low proportion of women observed on corporate boards varies across countries, which raises the question as to why? Based on institutional theory and the two sets of cultural dimensions proposed by Hofstede (1980) and House (2004), this study hypothesizes and tests whether this variation can be attributed to differences in the cultural settings. Our analysis of the representation of women on board for 5 European countries during 2006 reveals that the culture of a country indeed explains the observed differences. Of the cultural dimensions examined, power distance or a tolerance of inequality, uncertainty avoidance or a lack of tolerance for ambiguity, and masculinity or a preference for domination versus cooperation in superior/subordinate relationships have the highest explicative power for the differences in representation of women on boards that are observed around the world.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".