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Record W6990220475

Constructing a Victim of Violence:The Politics of “Safe Space” in Toronto’s LGBTQ Village

2017· other· en· W6990220475 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2017
Typeother
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsGovernment (linguistics)Agency (philosophy)Race (biology)Ethnic groupIdentity (music)
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.617

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0720.041
Scholarly communication0.0120.004
Open science0.0020.012
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0210.001

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.

Opus teacher head0.023
GPT teacher head0.320
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractno

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