Bibliographic record
Abstract
Safety Audits: A Catalyst for Change \n \nA feminist activist in Toronto, the author recounts her experience of managing the ‘women’s safety audits’ initiative for the Metropolitan Action Committee on Violence Against Women (METRAC), an organization established in 1984 in response to a large number of assaults on women in public places in the City with the founding mandate to be “a catalyst for change.” The ‘women’s safety audit’ – based on the fundamental belief that women are the experts of their own experience - was developed in 1989 by METRAC to address the problem of women’s unequal access to public space and their unequal participation in planning decisions that affected their sense of safety in their communities. In this article the author discusses the impact of the safety audits both in terms of effective changes to personal safety and empowerment for women and highlights the need that these initiatives – that have been adapted to different contexts and groups of women – are developed as partnerships between the women who have local safety concerns and key stakeholders such as elected representatives, city officials or the police, who have the authority to implement recommended changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.043 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 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".