Bibliographic record
Abstract
Certain racial/ethnic minority groups are greatly over-represented in Canada’s criminal justice system. While scholars examining this over-representation have pointed to issues of bias in the system, historically there has been a decided tendency on the part of several criminal justice system officials, legislators, some academics and media commentators to deny that such bias exists. Unfortunately, it is difficult to disentangle the causes of this over-representation, due to an informal ban on the release of race-based crime statistics in Canada. As Hagan (1998: xii) comments with respect to this issue, The reluctance to enumerate crime in racial terms is an unexpected product of an odd coalition of forces that, for a variety of dubious reasons, bans the necessary data collection. An unfortunate and little-recognized result of this ostrich-like behavior is complacent support for a posture of denial that pervades our justice system. An additional feature of the discourse on race and crime issues in Canada has been the tendency on the part of the media, both historically and in the current context, to engage in the racialization of crime, ‘part of a broader process that inferiorizes or excludes groups in the population’ (Tator and Henry, 2006: 8) — a topic to which we devote considerable attention in subsequent sections of this chapter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".