Generating an understanding of police brutality in the small island state of Trinidad and Tobago
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".