Bibliographic record
Abstract
There are over 600 missing and murdered aboriginal women across Canada. A long history of systemic racism has made these women extremely vulnerable to violent crimes. Most of their fates remain a mystery, but some murderers have been caught who are responsible for their deaths. I examined the news articles that cover the crimes of convicted murderers Robert Pickton and John Martin Crawford. Of the two, only Pickton is very well known. However, while the media covered his crimes extensively, much of the coverage is misleading. The aboriginality of the victims is downplayed, and other tactics are used to blame the victims and focus on the killer. The coverage surrounding John Martin Crawford uses similar misleading strategies, although there is significantly less of it. I argue that because the aboriginality of the victims was emphasized instead of downplayed in the coverage of Crawford’s murders, there was less interest in the cases. Most people will read about crimes when they can identify with the victims. While most of Pickton’s victims were aboriginal, the number of victims was so enormous and the details of the case were so grisly, that the aboriginality was downplayed to attract the attention that these other aspects gave the case. Crawford’s victims were all aboriginal women, but he killed fewer and was not seen as a threat. The media influences how people think about society. If the media continues to treat these types of crimes in this way, the ideas that fuel these crimes will also continue.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".