Toward Inclusive Excellence: Forging Inclusive Police Organizations through Leadership Training and Development
Bibliographic record
Abstract
Over the last several years, social and political events both in Canada and abroad, have resulted in the importance of diversity, equity, and inclusion (DEI) being brought to the forefront in society, and intensifying calls for police reform. Police organizations, like many others, have responded with a greater commitment to address these issues through things like DEI training, policy changes, and targeted recruitment of diverse employees. However, these efforts have failed to yield the desired change. Due to the tendency in police organizations to provide leadership training after employees have entered senior leadership positions, as well as the tendency to separate DEI training from leadership training, police leaders are being left ill-equipped to progress DEI goals, particularly from the standpoint of internal organizational culture and cultivating a respectful workplace. This Dissertation-in-Practice (DiP) explores a leadership problem of practice (PoP) faced by an urban Canadian police service—its leaders not having access to the comprehensive training and development required to develop inclusive leadership skills. This DiP details a plan to address the PoP through the implementation of a comprehensive leadership training and development strategy. Using inclusive leadership as the change leadership approach, Kotter’s 8-step model as an underlying change framework, and Plan-Do-Study-Act cycles supplemented by the Assess-Plan-Act model for monitoring and evaluating change, this DiP details a path toward forging inclusive police organizations through leadership training and development.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".