"Canada Can Do Better": An Exploration of Canadian News Media's Portrayal of Federal Penitentiaries and Prisoners During a Global Pandemic.
Bibliographic record
Abstract
COVID-19 has impacted everyone on a global scale and Canada is not immune. Canadian prisoners have faced many challenges and the media have been quick to document COVID-19 in justice facilities. How the media document these events plays a crucial role in our understanding of the criminal justice system. Therefore, it is imperative that researchers understand the media’s depiction of these atrocities because not only does it influences the public’s knowledge and understanding of the criminal justice system, but it has the potential to create policy change. Prior research on this subject matter has found that the media consistently offer an inaccurate and misconstrued image of prisoners, as being extremely violent and dangerous, and prisons being akin to a hotel. The current study adds to the existing literature by examining how online Canadian newspaper articles portray federally sentenced prisoners and the institutions they reside in during the pandemic. Conducting a content analysis of 85 articles published from a March 2020 to January 2021, the researcher sought emergent themes. The findings, limitations, suggestions for future research, and policy recommendations are discussed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".