Decolonization and Anti-Racism in Criminology
Bibliographic record
Abstract
Criminology and law are disciplines that have justified racism, the settler-state, and the cultural genocide of Indigenous peoples. In this chapter, we explore the strategies instructors who teach undergraduate legal courses in a criminology department use to meet the challenge of teaching about social inequities as they relate to law and how students were experiencing their instructors’ efforts. Five instructors who teach undergraduate law topics at a mid-sized Canadian criminology program told us about the strategies they used to incorporate anti-racist or decolonial ideas in their classes. Combined, these instructors taught 11 topics ranging from seminars with 30 students to lecture courses with over 200 students. We then surveyed students (n = 103) on the success of these strategies, and 73% (n = 75) indicated that their law courses helped them to understand social inequities as they relate to law. There was variability across courses where electives related to critical criminology and human rights met the social inequities goals to a higher degree (100% and 91%, respectively) than did their introductory and breadth law-related courses. According to student respondents, the most effective strategies that helped them appreciate social inequality in their law courses were textbook choices, including video clips and other media, through lectures, and by assigning academic articles that included different voices and perspectives. The authors share these findings to contribute data on the student experience that instructors can contemplate in developing their own anti-colonial and anti-racist pedagogical tools.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".