People in Crisis: Understanding the Impact of a Mental Health Response Unit on Police Culture
Bibliographic record
Abstract
The fatal Canadian police interactions involving Sammy Yatim, Robert Dziekanski, and Paul Boyd played a major role in developing and implementing mental health units (MHUs).Based on interview and direct observation data, this thesis examines the impact of a Canadian MHU on police culture.I argue that there are a number of possible cultures that can emerge within police organizations.This thesis demonstrates the pervasiveness of the perception of danger and the resulting camaraderie amongst MHU members.Specifically, I evaluate the perception of danger held amongst MHU members, their conceptions of partnership, and the importance of defending and assisting colleagues.Herein, I also argue that this MHU gives rise to an emerging servicebased conceptualization of police culture.Here, I recognize the fluidity of police culture by examining the service-focused nature of the MHU, the application of discretion, and the measurement of success and emotional commitment amongst MHU members.Table of Contents Abstract……………………………………………………………………………………………i Acknowledgements………………………………………………………………………………ii List of Appendices……………………………………………………………………………….iv Introduction…………………………………..…………………………………………………..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".