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Record W2902346655 · doi:10.35502/jcswb.77

First to serve and protect, then to lead: Exploring servant leadership as a foundation for Canadian policing

2018· article· en· W2902346655 on OpenAlexaffvenueabout
Les Sylven, Carolyn Crippen

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsServant leadershipEthical leadershipPolitical scienceFoundation (evidence)Context (archaeology)Public relationsLeadership styleLeadership studiesHarassmentSociologyCriminologyLaw

Abstract

fetched live from OpenAlex

Canadian police leadership is in the spotlight. In May 2017, three Canadian government studies concluded that the organizational culture inside Canada’s national police force was dysfunctional and appeared to lack a culture of leader­ship. Similar criticisms were levelled against other Canadian police agencies, and the new Commissioner of the Royal Canadian Mounted Police was specifically mandated to address workplace bullying, harassment, and abuse of authority. In August 2018, the Canadian Association of Chiefs of Police 2018 Executive Global Studies program called on police leaders to demonstrate “courageous leadership’” to address the predatory and exclusionary behaviours found inside their agencies. In this concept paper, an alternative view of leadership is put forward as a framework to address these chal­lenges. Servant leadership is a moral/ethical perspective that should intuitively resonate with police officers, particularly the next generation of police leaders. To explore the case for adopting this leadership approach in Canadian policing, its foundational concepts are presented. A description of the limited academic research on servant leadership in policing is described, and the article concludes with recommendations and questions to direct future research on exploring servant leadership in the context of Canadian policing.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.171
GPT teacher head0.369
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2018
Admission routes3
Has abstractyes

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