A Change is Gonna Come: Positive Perspectives on Change in Police Organizations
Bibliographic record
Abstract
This symposium examines and promotes positive organizational change within law enforcement agencies. Its main goal is to advance the understanding and application of Positive Organizational Scholarship (POS) principles in the complex landscape of policing by bringing together various perspectives, experiences, and research findings. Since its inception, Positive Organizational Scholarship (POS) has emphasized the importance of building strengths, fostering positive relationships, and initiating substantial changes in organizations. Based on this philosophy, the symposium adopts a POS approach, acknowledging that organizations thrive when they prioritize what works. This perspective offers a valuable lens to explore novel strategies and interventions that cultivate positivity, resilience, and flourishing in the face of work challenges within the law enforcement context. Ultimately, the symposium highlights innovative strategies, interventions, and approaches that facilitate positive transformations, improve relationships, and tackle challenges within law enforcement organizations. Through an in-depth examination of various initiatives, including procedural justice training, implicit bias training, and the restructuring of specialized units, as well as the negotiation of collective bargaining agreements, this symposium aims to contribute meaningful insights and promote positive change practices in law enforcement. Strengths-Based Change in a Police Specialty Unit: Empowering through Insider Action Research (IAR) Author: David Anderson; Des Plaines Police Department Positive Organizational Scholarship and Procedural Justice: A New Approach to Change Author: Madison Charman; Barrie Police Service Author: Rylan Simpson; Simon Fraser U. Encouraging Positive Interpersonal Interactions through Unconscious Bias Training Author: Hina Kalyal; London Police Service/Huron College Author: Mirit K. Grabarski; Lakehead U. Author: Megha Yadav; U. of Wisconsin, Milwaukee Author: Maria Mouratidou; U. of Cumbria, UK Implementation of the Peel Divisional Special Constable (DSC) Pilot Program Author: Cole Heffren; Peel Regional Police Service Rowing in the Same Direction: A POS Framework for Strategic Planning in Police Organizations Author: Hector Perez; Hamilton Police Service Author: Amanda Gill; Hamilton Police Service
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.065 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".