When Victims Look like Criminals: Rehumanizing Victim Representation in Serial Killer Cases
Bibliographic record
Abstract
In 2018 the Bruce McArthur serial killer case became the largest forensic homicide investigation in Toronto, Canada’s history. Victims of serial killers tend to be portrayed negatively by newspapers because they often embody stigmatized identities. However, this research asks, How do newspapers frame victims who belong in between marginalized and liberated identities? Under the frameworks of post-gay and intersectionality theory, the identities of many of McArthur’s victims reflect an opportunity to analyze how serial killer victims are puzzlingly framed by newspapers. Through an analysis of 277 articles in three major Canadian newspapers, the Toronto Star, the Globe and Mail, and the National Post, findings show that the framing of victims through textual accounts as nonpartisan is discrepant with their negative visual representations. While newspapers tend to simply frame these victims as belonging to Toronto’s gay village without layering stigma around queerness onto them explicitly, most articles provide close-ups, passport photos, and occasionally mugshots of victims, ultimately portraying them in undesirable ways. This inconsistent framing between textual description and image selection highlights the important role photos, activists, and non-profits can play when featured or quoted in newspapers as they can humanize and dignify victims in the absence of family or friends to do so. This textual-visual discrepancy shows that deeply racialized queer injustices can exist in newspaper framing despite the absence of overtly prejudicial narratives.
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.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".