Racialized, Gendered, and Sensationalized: An examination of Canadian anti-trafficking laws, their enforcement, and their (re)presentation
Bibliographic record
Abstract
Abstract In Canada, there are persistent allegations and some empirical evidence suggesting racialized police bias; certain (non-White) groups appear to face over-enforcement as criminal suspects and under-enforcement as victims. Yet, it is challenging to prove or disprove these claims. Unlike other countries, where governments routinely publish police-reported crime and criminal court data identifying the race/ethnicity of criminal suspects and victims, Canada maintains a ban on the publication of such data. In this article, using an intersectional and critical analysis, we examine 127 prosecuted (predominantly domestic sex) trafficking cases and explore related claims of racial and gender bias together with sensationalism in the enforcement of Canadian anti-trafficking in persons laws. Our findings align with other empirical research observing the racially selective identification and prosecution of sex trafficking cases through a heteronormative and gender binary lens. Whether real or perceived, racial—alongside gender, sexuality, economic, citizenship, and occupational—bias has significant adverse consequences for the equality, liberty, security, mobility, labour, and access to justice rights of the Indigenous, Black, Arab/Muslim and other racialized communities being policed. Our data reveal a clear and pressing need to publish race-disaggregated crime and criminal court data and to challenge deeply ingrained stereotypes using various means.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".