The Spectre of Punishment: The Relationships Between Media, Government, and the 2014 Not Criminally Responsible Reform Act
Bibliographic record
Abstract
This dissertation explores the relationships between Canadian news media coverage of individuals deemed ‘Not Criminally Responsible on Account of Mental Disorder’ (NCR) and the 2014 Not Criminally Responsible Reform Act. The NCR Reform Act was introduced after a small number of NCR cases received extensive media coverage in Canada––the Act made changes to the treatment of NCR accused which proponents claimed would protect the public by increasing the time NCR accused spend in treatment. Opponents, however, claimed the government was legislating in response to sensationalist media headlines. The dissertation uses a two-stage research design. The first stage comprises a content analysis of a sample of Canadian news media reporting on NCR from 2008-2020 and a thematic analysis of three high-profile cases. The second stage is a thematic analysis of all parliamentary debate transcripts relating to NCR or the NCR Reform Act from February 2013-August 2014, and all relevant government press releases from the same period. This dissertation demonstrates how the NCR Reform Act emerged out of an image of a problem––an image of NCR accused as dangerous, violent, and in need of punishment in the form of confinement. This image is co-created in a dialogic relationship by media reporting and government actors. The dissertation advances this argument through three empirical chapters which describe how media presents retributive confinement as a necessary response to unjust high-profile NCR verdicts which put the public at risk. When confinement is not ensured, media reporting calls for new laws to be passed to reduce suspect expert testimony and increase confinement. Government contributes to and enacts this culturally produced image of NCR through the NCR Reform Act––evidenced by citing high-profile cases as the impetus of the Bill, the alignment between media narratives and the Act’s legal changes, and the Bill’s desired impact upon the public perception of safety and justice. Overall, the NCR Reform Act represents a reinvention of the process of managing NCR accused in their own mediated image and cultural construction. The dissertation concludes by developing the theoretical implications of the work for understanding the varied relationships between media, government, and policy.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".