The Public Would Rather Watch Hockey! The Promises and Institutional Challenges of ‘Doing’ Public Criminology within the Academy
Bibliographic record
Abstract
While there has been growing academic dialogue concerning the need for, and value of, public criminology there has been little beyond theorizing and hypothesizing as to how one could actually ‘ do ’ public criminology. With this in mind, we set out to address this gap by implementing a departmental initiative that brought students into a for-credit course that was also open to the general public. This paper focuses on this enterprise and examines the promises and subsequent challenges of ‘doing’ public criminology within the academy. We deconstruct the academic and institutional shift toward ‘public’ engagement and intellectualism to better understand the “science-politics nexus” operating in criminology. We begin with a discussion of the present debates concerning public criminology and follow with a description of our public criminology colloquium series. We then discuss the promises and challenges we faced in the implementation of the colloquium and conclude by reflecting on how these personal challenges are representative of the broader institutional and organizational challenges facing public criminology.
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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 | 0.032 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.049 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".