Victimhood and Socio-legal Narratives of Hate Crime Against Queer Communities in Canada, 1985-2003
Bibliographic record
Abstract
This dissertation analyzes personal and institutional narratives that shape the Canadian phenomenon of anti-LGBT violence as hate crime and locate queers within and without the discursive figure of the responsible, legitimate and undeserving victim of hate crime. These socio-legal narratives were taken from interviews with LGBT community activists involved in anti-violence projects, mainstream and gay print news media reportage of two notable homicides, Parliamentary debates of the enhanced sentencing provision that sought to include ‘sexual orientation’ to the list of biased motivating factors, Senate witness testimony on the amendment to Canada’s hate propaganda statutes which sought to include ‘sexual orientation’ to the list of protected groups, interviews with police officers who had direct experience with anti-hate crime initiatives, and judicial reasons for sentence. Utilizing an interdisciplinary analysis and drawing on hate crime scholarship and victimology, this dissertation asks: how is legitimate and, consequently, illegitimate LGBT hate crime victimization being represented and constituted through Canadian socio-legal narratives? In revealing how socio-legal actors and institutions have positioned LGBT individuals discursively within or without legitimate victimhood, that is, within and without the status of innocent victim deserving of social empathy and socio-legal institutional response, my dissertation illustrates how the spectre of illegitimate victimization is repeatedly invoked in socio-legal narratives of anti-LGBT hate crime. My analysis of these narratives about queer victimization and hate crime suggests that the figure of the responsible, legitimate and undeserving victim of hate crime remains an elusive and unstable identity for the queer victim of hate crime. Insofar as hate crime scholars have argued that the mobilization of hate crime activism has produced a victim whose hate crime status ensures its legitimacy, I contribute to this scholarship by arguing that this status is particularly challenging for the queer victim of hate-motivated violence. I demonstrate that the resiliency of the figure of classic victimology’s self-endangering and risky ‘homosexual’ and the sustained ideological resistence to LGBT individuals as full citizens, despite their notable legal gains, positions LGBT individuals, particularly gay men, ambiguously, situating them conceptually both without and within legitimate victimhood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".