Bibliographic record
Abstract
Public criminology is concerned with public understandings of crime and policing and public discussions of such matters by criminologists and allied social scientists. For the purposes of this paper, these professionals are individuals identified by journalists on the basis of academic credentials or university affiliation as those who can speak to crime matters. This qualitative study investigates media statements made by criminologists and allied social scientists following the 2020 murder of George Floyd with two questions in mind: How have they responded to debates over reforming, defunding, and abolishing police? What insight can these responses provide about public criminology more generally? I analyze statements offered by criminologists in news reports and on Twitter using Qualitative Media Analysis, an approach that emphasizes the processes through which discourse is presented to audiences. I argue that recent criminological debates in the media concerning the future of policing have exposed unresolvable tensions among scholars who engage in the practice of public criminology, suggesting that the public is not receiving coherent, authoritative messages about these issues. The findings also raise questions about public criminology and illuminate new concerns regarding scholarly expertise related to knowledge claims and credibility relative to social justice.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".