Leadership approaches in law enforcement: A sergeant’s methods of achieving compliance with racial profiling policy from the front line
Bibliographic record
Abstract
This research aims to fill a void in the extant policy implementation literature that has overlooked the leadership contribution of sergeants to the successful adoption of policy decisions by front-line police officers. Using a qualitative approach and a sociological institutionalism perspective, and focusing on the racial profiling policy of a large North American municipal police organization, 17 sergeants representing 17 divisions (precincts) were interviewed. This research does not aim to assess the efficacy of the selected policy but, rather, examines leadership and supervisory perspectives relating to implementation and compliance. The findings demonstrate the methods used by sergeants to influence and achieve the compliance of front-line police officers with the racial profiling policy. Methods include auditing, being present, training, encouraging, rewarding, and disciplining. To explain these methods, it is theorized that sergeants blend two leadership approaches to ensure front-line officers conform to the racial profiling policy: an authoritative leadership approach and a supportive leadership approach. This study emphasizes the leadership contributions of sergeants when attempting to implement perceived controversial or unpopular policy—in this case, racial profiling policy—in a police organization and contains implications for law enforcement leaders, oversight committees, policy writers, and all government legislators who oversee public safety and security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".