“Everybody Loves a Redemption Story around Election Time”: Rob Ford and Media Construction of Substance Misuse and Recovery
Bibliographic record
Abstract
The crack cocaine scandal that embroiled former Toronto Mayor Rob Ford presents an opportunity to explore how we think and talk about substance (mis)use and recovery. Examining 1,836 articles from four Canadian newspapers, we analyze the ways news media frame Ford's use of crack cocaine. We find that Ford's drug use was often linked to a police investigation into gangs and guns, and much was made of his association with “Somali” drug dealers. Not only does this framing perpetuate prevailing stereotypes (crack cocaine use by racialized individuals living in poor and violent communities), but also it encourages the public to consider drugs a criminal justice issue and contributes to the stigma associated with drug use. Moreover, news media repeatedly suggested that Ford's problematic drug use could be solved if he took a leave from his job and entered a treatment facility. However, Ford's refusal to express shame and seek immediate treatment made him unworthy of compassion and instead rendered him deserving of censure. We argue that news media promoting a narrow pathway to addiction recovery and redemption ignores the realities of problematic drug use and justifies the continued marginalization of those who fail to meet this strict code of conduct.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".