Courage in the Face of Hate: a curricular resource for confronting anti-LGBTQ violence
Bibliographic record
Abstract
Introduction Current Canadian data (Dowden and Brennan, 2012) and a handful of research projects (Faulkner, 2006/2007; 2006; Burtch and Haskell, 2010; Taylor and Peter, 2011) make it clear that the environment in many Canadian communities remains unsafe for LGBTQ (lesbian, gay, bisexual, trans, queer) people. In light of the isolation and violent victimisation that both documented and undocumented hate crime victims face, it is vital that we develop meaningful strategies both to support the victims of LGBTQ hate crimes and reduce future occurrences. With this in mind, Egale Canada and Dr Barbara Perry partnered to create Courage in the Face of Hate (CFH), which aimed to create safer spaces where story-telling and education could take place among victims of hate crime and hopefully aid in their journey of healing. These sharing activities were also intended to build courage within LGBTQ communities, thus enabling and encouraging victims and witnesses to report crimes to police. Finally, by humanising LGBTQ people in a resultant video, and showing it to students who may not necessarily know an LGBTQ person, we sought to reduce fear and dispel prejudice, with the long term hope of reducing the rates of violence against LGBTQ communities. This chapter aims to lay out the rationale for the project, the strategies we engaged, our experiences in conducting the project, and a summary of our final ‘products,’ including findings and, of course, the video. The contexts for anti-LGBTQ violence Anti-gay and anti-trans hate crime occurs as a result of the heterosexism and cissexism that permeate societal institutions (Herek, 1992). Heterosexism is ‘an ideological system that denies, denigrates and stigmatises any non-heterosexual form of behavior, identity, relationship or community’ (Herek 1992: 89). Cissexism is the correlative ‘belief that transsexuals’ identified genders are inferior to, or less authentic than, those of cissexuals’ – those whose gender identity is congruent with the sex assigned to them at birth – and the attendant systems of oppression (Serano, 2007: 12). Consider, for example, laws that recognise only opposite-sex marriages and deny same-sex couples the opportunity to adopt children, or receive tax benefits – laws that existed in Canada until as late as 2005.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".