Data, Denials and Confusion: The Racial Profiling Debate in Toronto
Notice bibliographique
Résumé
On 19 October the Toronto Star began publication of a series of articles on the controversial topic of race and crime. In addition to reviewing previous Canadian research on this issue, the Star provided its own, original analysis of data derived from the Toronto Police Service's Criminal Information Processing System (CIPS). The CIPS data set, received from the police through a freedom of request, contains on over 480,000 incidents in which an individual was either charged with a crime or ticketed for certain types of traffic offences. (1) The final data set is supposed to represent the total population of criminal charges (approximately 800,000) laid by the Toronto Police from late 1996 until early 2002 (see Rankin, Quinn, Shephard, Simmie, and Duncanson 2002a). The Star's analysis revealed that black Torontonians are highly over-represented in certain charge categories--including drug possession. The Star maintains that this pattern of over-representation is consistent with the idea that the Toronto police engage in racial profiling (Rankin et al. 2002b). The Star's analysis also reveals that blacks may be treated more harshly after arrest than their white counterparts. In particular, white offenders are more likely to be released at the scene, while black offenders are more likely to be detained and taken to the station for processing. Furthermore, once at the station, black offenders are much more likely than whites to be held in custody for a bail hearing. The Star maintains that these racial differences remain after other relevant legal factors have been taken into statistical account (Rankin et al. 2002a). In response to the Star series, the Toronto police vehemently denied all allegations of racial bias. Chief Fantino declared that [w]e do not do racial profiling ... There is no racism ... We don't look at, nor do we consider race or ethnicity, or any of that, as factors of how we dispose of cases, or individuals, or how we treat individuals (quoted in Racism 2002: A14). Craig Brommel, president of the Police Association, stated in a news release that [n]o racial profiling has ever been conducted by the Toronto Police and we question the Toronto Star's interpretation of its statistical information (quoted in Porter 2002: A6). These sentiments were echoed by several local politicians. Mayor Lastman, for example, declared that: I don't believe that the Toronto police engage in racial profiling in any way, shape or form. Quite the opposite, they're very sensitive to our different communities (quoted in Analysis 2002: A9). Even Norm Gardiner, chair of Toronto's civilian--and supposedly non-partisan--police oversight board, claimed that he was confident that the Toronto police did not engage in racial profiling. In fact, he went so far as to suggest that the researchers involved in the Star series just wanted to stir up controversy during a period of excellent police-minority relations: Some of the people involved, who keep on bringing this stuff up ... they make a living out of social unrest (quoted in Analysis 2002: A9). Nonetheless, despite all of their denials, police officials provided no systematic critique of the Star's analysis in the four months following the release of the race-crime series. Nor did they provide any new that might challenge the racial-profiling hypothesis. All that changed, however, at a Police Services Board meeting held on 20 February 2003. On this occasion Chief Fantino produced his own experts on racial profiling, who subsequently slammed the Star report as being based on faulty research. Edward Harvey, the University of Toronto sociology professor hired by the police to conduct the re-analysis of the Star data, concluded that his independent review results do not provide evidence of systemic racial profiling being practiced by the Toronto Police Service (Harvey 2003; see also Granatstein 2003: 22). …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».