Bibliographic record
Abstract
Geographical areas with high concentrations of impoverished racialized groups\ntend to experience disproportionate rates of violence in Canada. As news media heavily\nfocuses on crime reports, violence often comes to characterize the affected\nneighbourhoods. News reports can impact audience levels of fear and scholars argue that\ndisproportionate reporting of crime-related events can instil fear among the public. To\ndate, there has been no study that examines a moral panic of neighbourhoods. Therefore,\nthis thesis examines how the racialization of crime and the criminalization of place\ncoalesce to create a moral panic of a neighbourhood. To examine the media???s role in\ncreating fear, two Toronto newspapers were sampled over a 14-year period. A frame\nanalysis was conducted to investigate how Toronto newspapers framed Kingston-\nGalloway between 1998-2012. Findings suggest that Toronto newspapers racialize crime\nand criminalize place, which may aid in the construction of a moral panic of a\nneighbourhood.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".