Flooding the Zone, Challenging State Secrecy: Newsmaking Criminology in Pandemic Times
Bibliographic record
Abstract
Purpose – During the COVID-19 pandemic, Canadian jurisdictions have varied in terms of their reporting of COVID-19 cases among prisoners and prison staff. Engaging with literatures on the policing of criminological knowledge and prison opacity, this chapter examines how multiple approaches to newsmaking criminology including blog posts, op-ed writing, report publishing, and expert commentary can challenge state secrecy in ways that help generate proactive disclosure of additional information about the impact and management of the coronavirus behind prison walls.Methodology/Approach – The authors explore how “flooding the zone” of public debates on pandemic management with the limited, incomplete data made available by authorities works as a knowledge mobilization and research strategy.Findings – The analysis in this study reveals how a newsmaking criminology approach can help researchers access previously unpublished information from Canadian prison authorities that is crucial to understanding prison policy, practice, and outcomes related to COVID-19.Originality/Value – This chapter highlights the value of newsmaking criminology as a means of communicating and mobilizing criminological knowledge, as well as generating data in the service of emancipatory research and advocacy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".