Criminological Cliques: Narrowing Dialogues, Institutional Protectionism, and the Next Generation
Bibliographic record
Abstract
Abstract This chapter presents three related challenges faced by the ‘discipline’ of criminology, with the goal of promoting dialogue about the field's future. It first argues that although criminology has achieved disciplinary status with discrete areas of specialization, it is vitally important that criminological research and education draw on the range of other disciplinary knowledge that intersect with criminology. Second, it explores how the development of research branches in many criminal justice agencies, who are rightly concerned with the everyday pragmatics of policing or punishing, can (and in some cases do) shape and restrict research possibilities. It examines how emerging forms of institutional protectionism restrict the production of critical criminological knowledge. These observations may be applicable to other countries, but the focus is on the research landscape in Canada. Finally, the chapter considers how commitments to intellectual diversity and the restrictions imposed on certain types of ‘critical’ scholarship can complicate future criminologists' research and education. These three themes are linked by a broader interest in criminological knowledge, its structure and progression as well as by a need to discuss how various institutional ‘boundaries’ shape our theorizing, research questions, types of analysis, and scholarly standards.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".