We Killed Them First: How the Robert Pickton Investigation Revealed Systemic and Intersectional Discrimination within Canada
Bibliographic record
Abstract
Robert Pickton is often considered to be Canada’s most notorious serial killer. By his own estimation, he murdered 49 women over at least 5 years (Craig, 3). The Pickton case brought attention to fundamental issues within the Canadian justice system and highlighted systemic inequities. From the outset, the police failed to take the issue of missing and murdered women seriously because the women were considered expendable. As members of several marginalized communities at the intersection of race, low socioeconomic status, and stigmatized labour, the loss of these women was considered acceptable. I argue that the intersection of these characteristics that were “accepted” as defining Pickton’s victims demonstrates that the assumptions and biases of those who enforce the law (police and judges) can seriously impair the system's ability to create equitable outcomes. Utilizing media analysis, this paper looks at the language commonly found in articles about Pickton and his victims to demonstrate the role intersectional characteristics played in crafting certain narratives to the public.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.067 | 0.027 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".