Community, Housing, and Crime: Framing the News Coverage of Lawrence Heights and Rexdale
Bibliographic record
Abstract
Why is there less media coverage or public outcry when Black or racialized people lose their lives in Lawrence Heights and Rexdale? My dissertation started with this simple question. By studying the intersection of media, housing, community and crime, my dissertation sheds light on how mainstream and independent new sources contribute to stereotypes and metaphors that influence the public perception of Lawrence Heights and Rexdale. Starting in 1960, ending in 2020, I collected news from the Toronto Star, Globe and Mail and Share in the three categories of housing, crime, community. I used 9 variables to determine what type of news appeared in a higher frequency to show how independent, Black news media has told a more nuanced story. My research found there is work to do in countering the high frequency of crime stories in the mainstream news, and the presence of independent publications like Share are vital in presenting counternarratives that give a voice to the community. As well as representing how residents, community groups and activists have come and are coming together to reclaim their right to the city.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".