The Perceptions of Police-Black Civilian Deadly Encounters in North America among Black Immigrants in a Western Canadian City
Bibliographic record
Abstract
This study investigates black immigrants’ perceptions of police-black civilian deadly encounters in North America. Twenty semi-structured, in-depth interviews were conducted among black immigrants in Edmonton, western Canada. The respondents perceived racism, police brutality, black criminality, gun violence and police perception of black people as ‘violent’ as the causal factors in deadly encounters. There was also the perception of criminal injustice and conspiracy among the agents of the criminal justice system (CJS) in the treatment of victims and suspects. This study suggests that personal and media experiences can influence how people de/re/construct deadly encounters and the treatment of victims and suspects by the CJS. Findings also reveal that when members of a racial (immigrant) minority perceive themselves as the target of a discriminatory CJS, they may adopt cautious and cooperative actions rather than aggressive or deviant behaviour to avoid criminalization and victimization. The study concludes that the perception of criminal injustice in police deadly violence against black (minority) civilians could influence: (i) where (black) immigrants locate themselves within the CJS in North America, and (ii) how (black) immigrants perceive and respond to the agents of the CJS, such as the police, when they encounter them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".