Constructing a Victim of Violence:The Politics of “Safe Space” in Toronto’s LGBTQ Village
Bibliographic record
Abstract
As North American LGBTQ communities have become increasingly visible andsubsequently been targeted by violence for this visibility, urban LGBTQ villages have oftenbeen framed by activists as “safe spaces” where these communities can protect themselves.The way violence is constructed within these spaces, however, has implications for who thespace is ultimately seen to belong to, especially as police involvement in notions of LGBTQprotection increases. This thesis examines iterations of “safe space” activism in the Canadiancity of Toronto during two moments in the 1980s and 1990s, focusing in particular on twoorganizations, the Toronto Gay Street Patrol (1981-1984) and the Church/WellesleyNeighbourhood Police Advisory Committee (1992-2000). I argue that in response to anti-LGBTQ violence, attempts to form “safe space” in Toronto’s LGBTQ village during the1980s and 1990s often constructed a particular victim of violence whose safety was central toLGBTQ community, and which increasingly coincided with the figure welcomed by localbusiness and residential interests. As police presence in the neighborhood increased, theprivileging of these interests allowed for the public framing of groups that were seen as badfor business owners and residents as also threats to LGBTQ safety, ultimately justifying theirforced removal from the village.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.072 | 0.041 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".