Bibliographic record
Abstract
In January 2011, Toronto Police Constable Michael Sanguinetti addressed a small group of York University students on campus safety. Prefaced by the statement ‘I’m told I’m not supposed to say this’ he went on to advise that, ‘women should avoid dressing like sluts in order not to be victimized’ (Kwan 2011). While his intention might have been to protect women, his comments that ‘slutty’ women attract sexual assault perpetuated the long-standing myth that victims are responsible, or somehow ‘are asking’ for the violence used against them. In response to PC Sanguinetti’s comments, Toronto residents Heather Jarvis and Sonya Barnett translated their anger at the ways women were slut-shamed and victim-blamed, into political activism. Creating a website and Facebook and Twitter accounts, the women invited the public to join them for a ‘SlutWalk’ to the Toronto Police Headquarters to vent their frustration. On 3 April 2011, the first SlutWalk set off from Queen’s Park in Toronto, attended by thousands. Although the organizers asked people to dress in their normal, everyday clothing to demonstrate the ways that sexual assault occurs no matter what women wear, a number of attendees showed up in ‘provocative’ attire to make a statement that no matter how they dress, they do not deserve to be assaulted. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.452 | 0.232 |
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".