Exploring Factors Perpetuating the Underrepresentation of Women CAOs in Local Government
Bibliographic record
Abstract
Despite women's progress in various professional domains, a persistent gender disparity remains evident in local government's most senior non-elected leadership roles. The underrepresentation of women in municipal city management continues, even though women comprise 47.4% of the Canadian labour force (Catalyst, 2020). This research paper aims to understand the underrepresentation of women in municipal Chief Administrative Officer (CAO) roles by examining theoretical perspectives and drawing from recent qualitative research into the gender imbalance in public sector leadership by DeHart-Davis et al. (2020). This research paper aims to shed light on the need for substantive change by unravelling the complexities surrounding the pervasive underrepresentation of women in municipal CAO roles. Secondary data collected on 110 municipalities in Ontario’s Greater Golden Horseshoe region is analyzed and compared with other research findings to assess the gender balance among CAOs.The research findings contribute significantly to the scholarship on gender equality in Canadian local government leadership. This research paper offers insight for students, practitioners, and municipal leaders seeking to understand and promote equality in senior administrative positions. This research aims to contribute to existing research on systemic inequality in local government and understand the importance of a more inclusive and diverse leadership landscape.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| 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".