Hate crime law & social contention : a comparison of nongovernmental knowledge practices in Canada & the United States
Bibliographic record
Abstract
Hate crime laws in both Canada and the United States purport to promote equality using the language of antidiscrimination law. National criminal codes in both countries authorize enhanced punishment for crimes motivated by “sexual orientation” but not “gender identity” or “gender expression.” Cities and states in the United States have also adopted hate crime laws, some of which denounce both homophobic and trans-phobic crimes. Hate crime penalty enhancement laws have been applied by courts in both Canada and the United States to establish a growing jurisprudence. In both countries, moreover, other hate crime laws contribute to official legal knowledge by regulating hate speech, hate crime statistics, and conduct equivalent to hate crimes in schools, workplaces, and elsewhere. Yet, despite the proliferation of hate crime laws and jurisprudence, governmental officials do not control all legal knowledge about hate crimes. Sociological “others” attend criminal sentencing proceedings and provide support to hate crime victims during prosecutions, but they also frame their own unofficial inquiries and announce their own classification decisions for hate-related events. In both Canada and the United States, nongovernmental groups contend both inside and outside official governmental channels to establish legal knowledge about homophobic and trans-phobic hate crimes. In two comparable Canadian and American cities, similar groups monitor and classify homophobic and trans-phobic attacks using a variety of information practices. Interviews with representatives of these groups reveal a relationship between the practices of each group and hate crime laws at each site. The results support one principal conclusion. The availability of local legislative power and a local mechanism for public review are key determinants of the sites and styles of nongovernmental contention about hate crimes. Where police gather and publish official hate crime statistics, the official classification system serves as both a site for mobilization, and a constraint on the styles of contention used by nongovernmental groups. Where police do not gather or publish hate crime statistics, nongovernmental groups are deprived of the resource represented by a local site for social contention, but their styles of contention are liberated from the subtle influences of an official hate crime classification system.
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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".