Bibliographic record
Abstract
There is a growing body of literature on the nexus of media, race, and crime, which reveals that crime is exaggerated in mainstream media and that these same venues tend to racialize crime and criminalize race. The impact of this is that inaccurate public perceptions about the frequency, seriousness, and demographic distribution of crime are reinforced. Interestingly, however, there have been no focused efforts to explore the ways in which crime is featured within the media targeting specific racial and ethnic communities. We know little about whether such outlets reproduce these patterns. This pilot study is intended to initiate an examination of the representation of crime news in Canada's ethnic media, exploring the patterns of crime reporting in such outlets and comparing the ways in which such news is presented to different audiences. We conducted a content analysis of two English-language newspapers in the Greater Toronto Area, which nonetheless serve specific racial and ethnic communities. Quantitative (e.g., frequency) and qualitative (e.g., themes) findings from the study offer insights into crime reporting patterns, as well as the nature of crime coverage in the studied newspapers.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".