Women, the Forgotten Majority: Achieving Gender Equity at the City of Toronto - A Critical Analysis
Bibliographic record
Abstract
A critical anti-racist feminist analysis is used to examine the City of Toronto's current approach to gender equity and to consider how the City can move forward based on the discussion of Toronto's issues and challenges and other cities' successes. Written from the perspective of one member of a 14-member City of Toronto women's advisory committee, it examines the City's approach to diversity in general and gender equity in particular. The analysis finds serious flaws in the City's current approach, related to a lack of gender equity structures and mechanisms, a lack of interest in, and resources allocated to, such structures and mechanisms, and a lack of civic engagement of women, which forms a part of the City's overall democratic deficit. Structures and mechanisms implemented in other cities in order to promote gender equity are explored in order to provide the City with successful possibilities to consider. A proposed six-stage model categorizes various levels of commitment that cities have demonstrated towards achieving gender equity. This model allows cities, such as Toronto, to assess their individual progress on gender equity relative to other cities and to better understand the need to increase their efforts. Lastly, recommendations to the City of Toronto to enhance its gender equity approach are discussed. Despite the limitations of this study, the author believes it was highly necessary to document and disseminate the issues related to the City of Toronto's approach to gender equity in order to open up productive dialogue between the City and the community and to motivate effective, equity-enhancing change in a timely manner.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".