‘Beauty and bullets’: A content analysis of female offenders and victims in four Canadian newspapers
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
Media-generated discourse can provide a framework for its consumers to construct representations of the world they live in. These representations, however, are often disproportionate to the true incidence of crime or risk of victimization. In order to examine the extent to which the gender of the offender or victim impacts portrayals of crime, content and discourse analyses were carried out on four Canadian city newspapers over a span of 30 years. The results from the 1190 sampled crime articles revealed that, although portrayals of female offenders accurately depict them as generally lower-risk, both female offenders and female victims were treated equivocally. Women offenders were dichotomized into sexualized bad girls or malicious black widow archetypes. Similarly, female victims were depicted either as bad victims who were blamed for their circumstances, or good victims who garnered sympathy through negative portrayals of the offenders. The findings are discussed within the context of gender differences surrounding the social discourse of violence, particularly chivalry.
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 | 0.000 | 0.000 |
| 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