Exploring How Women on Corporate Boards Cope With Gender Bias
Bibliographic record
Abstract
Gender bias may cause organizations to lose the values that women bring to the workplace in leadership positions and may thwart women from reaching their personal goals. The purpose of this qualitative descriptive multiple case study was to explore gender bias and its influence on women on corporate boards, their roles, appointment, and the need to develop coping strategies to deal with gender bias to execute their roles. The conceptual lens used was Tajfel and Turner's social identity theory to explain the basis for intergroup discrimination, and Eagly and Karau's role congruity theory of prejudice to explain the exclusion of women from corporate boards as a result of gender bias. The research question focused on identifying gender bias and experiences of women on a corporate board. Social media and snowball sampling were used to recruit 6 English-speaking women on corporate boards who had experienced gender bias at the time of their appointment and in their roles on corporate boards in the public and private sectors in provinces and territories throughout Canada. Data sources included interviews, journaling, and analysis of physical artifacts such as government reports and databases of women on corporate boards. Using Yin's 5 phases of analysis, the study identified 7 emergent themes in the data sources: discrimination, harassment, organizational climate, well-being, disruption, empowerment, and leading. The study's potential for positive social change resides within its potential to promote the internal transformation of women as they deal with bias. Men also need an improved understanding of their perceptions of women in the governance structures of society to help reduce gender bias toward women.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".