Racismo sistémico en las intervenciones policiales en Canadá: realidades y discursos denegatorios
Bibliographic record
Abstract
En poco menos de una década, la mayoría de las principales ciudades canadienses han comenzado a exigir a los servicios de policía municipales que coloquen sus datos operativos a disposición de investigadores externos para documentar la problemática del perfilamiento racial (racial profiling en inglés, profilage racial en francés, es decir la práctica ilegal de basar una decisión policial en el tipo de identidad “racial”, real o supuesta, de una persona). Así se produjeron informes de investigación en Ottawa (Foster, Jacobs y Siu, 2016; Foster y Jacobs, 2019), Toronto (Ontario Human Rights Commission, 2018; Wortley y Jung, 2020; Wortley, Laniyonu y Laming, 2020), Vancouver (Manojlovic, 2018), Edmonton (Griffiths, Montgomery y Murphy, 2018), Montreal (Armony, Hassaoui y Mulone, 2019; Armony, Boatswain-Byte, Hassaoui y Mulone, 2023) y Halifax (Wortley, 2019). Esta tendencia se inscribe en un movimiento social más amplio que cuestiona el trato discriminatorio de la policía hacia minorías racializadas y que llegó a su apogeo con Black Lives Matter en Estados Unidos y con las protestas generadas allí y en otros países, incluyendo a Canadá. En tal contexto, surgen acusaciones de racismo sistémico imputado a las fuerzas de seguridad y se formulan exigencias de reforma policial a nivel organizacional más allá de la distribución de sanciones individuales por casos específicos (Davis, 2018; Maynard, 2018).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".