The diversity-dissent paradox: Navigating police and university recruitment challenges amidst campus protest dynamics
Bibliographic record
Abstract
Amidst the current wave of social activism sparked by the ongoing crisis in Israel-Palestine, universities across the United States, Canada and Australia are increasingly turning to law enforcement to address social unrest on their campuses. This has been met with vocal support from local politicians in the United States, some of whom advocate for the deployment of the National Guard. However, such reliance on state power comes at a time when law enforcement agencies and universities are grappling with significant challenges in recruiting new cadets and students, particularly from racialised and/or otherwise marginalised communities. This conceptual article delves into the intricate paradox facing both institutions today. While there is a pressing need for a police force that reflects the diversity of the communities it serves, much of police action, often involving the use of force, is directed at these very communities as they voice their grievances against perceived social injustices. Drawing on insights from anticolonial literature, historical and contemporary analyses of police interactions, this article elucidates how policing has historically served to uphold existing power structures and how, in certain cases, the continued alignment of police forces with elite interests exacerbates the difficulty of diversifying the force.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.047 | 0.041 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".