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Record W4312852857 · doi:10.24043/isj.409

Mediate or litigate: An evaluation of citizen and police officer perspectives on the use of mediation to resolve citizen-police conflict in Trinidad and Tobago

2022· article· en· W4312852857 on OpenAlexvenueno aff
Wendell C. Wallace, Karen Lancaster‐Ellis

Bibliographic record

VenueIsland Studies Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsMediationOfficerPolitical scienceCommunity policingConflict resolutionCriminologyConflict managementPublic relationsLawSociology

Abstract

fetched live from OpenAlex

Given the confrontational nature of citizen-police interactions, conflict between both groups is inevitable. On one hand, it is argued that citizen-police conflict and complaints against the police must be properly ventilated; however, on the other hand, it is argued that existing dispute resolution mechanisms are biased in favor of the police. With this in mind, police departments and community residents are increasingly seeking alternative mechanisms to resolve citizen-police conflicts as well as citizen complaints against police officers and mediation has emerged as a forerunner. Using a quantitative approach, this exploratory study concurrently evaluated citizen and police officer perspectives regarding the role of mediation as an alternative to judicial and other legal based mechanisms to resolve citizen-police conflicts in Trinidad and Tobago. The study is premised on ‘islandness’ and the findings indicate that generally, both citizens and police officers are willing to utilize mediation to resolve citizen-police disputes, however, there are some disparities over the issue by gender. The paper concludes by advocating for a complaints management system that includes mediation within a consultative framework focused of behavioral improvements to be implemented within the Trinidad and Tobago Police Service.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.972

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.277
GPT teacher head0.448
Teacher spread0.171 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2022
Admission routes1
Has abstractyes

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