Invisibility cloaks, prisons, and a pandemic: Did COVID-19 render the prison invisibility cloak ineffective?
Bibliographic record
Abstract
This thesis acknowledges the importance of examining news media representation of prisons, and more specifically, news media representation of Canadian prisons during the COVID-19 pandemic. A thematic qualitative and quantitative content analysis of news coverage of prison before and during the COVID-19 pandemic was undertaken to analyze how news media organizations communicate meanings and messages about punishment and prison to the general public. Utilizing a social constructionist approach, I examined how the news media frames coverage of correctional institutions, consequently shaping public understanding of punishment and prison which may impact correctional policy. This thesis addressed the following questions: 1) Was there an increase in news media coverage of prison during the COVID-19 pandemic? Has coverage during the COVID-19 pandemic made the prison more visible? 2) Does the news media coverage of correctional institutions during the COVID-19 pandemic reinforce traditional myths and stereotypes surrounding punishment and prison? Or challenge them? 3) Is the news media representation of correctional institutions during the COVID-19 pandemic consistent with coverage of traditional prison newsworthy items which focus on discrete incidents? Or does the coverage reflect newer, broader systemic newsworthy issues, namely, reform? The findings demonstrate that the COVID-19 pandemic did not quantitatively bring more visibility to prisons as assessed by the amount of news items, however, qualitatively it appears COVID-19 brought more visibility to prison issues. While some traditional prison stereotypes are still present in the news media and were reinforced during the pandemic, other myths and stereotypes were challenged, or were rare. Lastly, the results demonstrate that although traditional prison newsworthy items were still often reported in the news, discussions of prison reform were prevalent in the sample.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".