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Record W4360965409 · doi:10.24043/isj.415

Generating an understanding of police brutality in the small island state of Trinidad and Tobago

2023· article· en· W4360965409 on OpenAlexvenueno aff
Tamara Forde

Bibliographic record

VenueIsland Studies Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsnot available
Fundersnot available
KeywordsPolice brutalityCriminologyPolice scienceSociologyPolitical scienceCriminal justice

Abstract

fetched live from OpenAlex

Trinidad and Tobago (TT), like some other island states, experiences frequent incidents of police use of excessive force and police brutality which often involve individuals from low-income communities. The concepts of police use of excessive force and police brutality are often used synonymously. However, they can be distinguished by examining police brutality through an island studies lens. Hence this study aims to improve how police brutality can be understood and distinguished from police use of excessive force by applying an island studies theoretical framework. Additionally, because there is a dearth of academic literature on how colonialism has influenced police brutality in contemporary TT, and because little is known about how victims experience police brutality, the study also addresses those gaps. The current study bridges these gaps by conducting a phenomenological study to generate an understanding of the lived experiences of victims of police use of excessive force in low-income communities in Trinidad. Based on the analysis of the data collected from in-depth interviews with 18 research participants, six themes emerged. The findings were then analyzed through the lens of island studies and the implications of those findings were discussed. The study ends by making several recommendations.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.140
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.166
GPT teacher head0.374
Teacher spread0.208 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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