Beyond Reputation Management: An Auto-Ethnographic Examination of Diversity, Equity, and Inclusion in Canadian Policing
Bibliographic record
Abstract
Policing organizations play a vital role in increasing diversity and recruiting individuals from diverse backgrounds. However, they face the challenge of reconciling merit-based hiring with the influence of social capital, necessitating a stronger focus on equity policies. This paper delves into this intricate landscape, leveraging both personal experiences and the framework of employment equity laws. It also draws upon insights gleaned from the Sandhu case to advocate for a holistic approach that encompasses cultural and legal changes to combat the issues surrounding “otherness” within policing. Through a comprehensive exploration of these cases, this paper unravels an intricate tapestry of the challenges faced by policing organizations. It provides valuable insights into nurturing diversity, equity, and inclusion within these entities, addressing issues like othering and racial profiling. This paper underscores the vital importance of public security organizations embracing equity, diversity, and inclusion to better fulfill their mission of serving the communities they protect. By adopting these principles, organizations can improve their effectiveness and make substantial contributions to fostering a more equitable society, transcending the confines of mere reputation management.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".