Decolonizing Criminology: Exploring Criminal Justice Decision-Making through Strategic Use of Indigenous Literature and Scholarship
Bibliographic record
Abstract
Post-secondary institutions have been increasingly called upon to decolonize pedagogy and syllabi. Minimal research has examined decolonization efforts within criminology curricula despite such classes often exploring structural racism in discussions of the overrepresentation of Indigenous, Black, and other racialized persons in the criminal justice system. Through a content analysis of multiple written assignments – written by 25 undergraduate students enrolled in a decision-making in criminal justice class offered at a university in western Canada – this study explores how an instructor decolonized their course through the strategic use of Indigenous literature and scholarship. The results indicate a single course does not provide enough time to unravel the complex connections between colonialism and Indigenous peoples’ involvement in the justice system. Further, students have a desire to engage in difficult conversations about racism and colonialism. Take-aways for consideration by instructors and administrators working towards decolonizing curricula are discussed.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".