Conservative Politics, Sacred Cows, and Sacrificial Lambs: The (Mis)Use of Evidence in Canada's Political and Penal Fields
Bibliographic record
Abstract
This article explores the federal government's justifications for its decision to cancel Canada's prison farm program, and demonstrates how Harper's Conservatives used talk of “evidence,” “science,” and “research” to appear to be performing good, modern governance. We argue that developments within the penal field are deeply intertwined with activities within the political field—and are more clearly understood when situated within the broader context of politics, science, and the strategic (mis)use of evidence. Scholars must be careful in assuming the state is actually engaging evidence rather than doing so only at the level of rhetoric and discourse. Cet article explore les justifications utilisées par le gouvernement fédéral par rapport à sa décision d'annuler le programme canadien de prisons‐fermes, et démontre comment les conservateurs d'Harper ont utilisé les notions d’‘évidence’, de ‘science’ et de ‘recherche’ pour donner une apparence de bonne performance et de gouvernance moderne. Nous affirmons que les développements du champ pénal sont profondément imbriqués avec les activités à l'intérieur du champ politique—et sont mieux comprises lorsque situés dans le cadre plus large des politiques, de la science et des stratégies de (mal)utilisation d’évidences. Les spécialistes devraient être prudents lorsqu'ils assument que l’État prend réellement en compte les évidences alors qu'il est en fait seulement au niveau de la rhétorique et du discours.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.030 | 0.052 |
| Scholarly communication | 0.024 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".