Racialized, Gendered, and Sensationalized: An examination of Canadian anti-trafficking laws, their enforcement, and their (re)presentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it