Rejoinder to Satzewich and Shaffir on “Racism versus Professionalism: Claims and Counter-claims about Racial Profiling”
Notice bibliographique
Résumé
In their article Racism versus Professionalism: Claims and Counterclaims about Racial (2009), Vic Satzewich and William Shaffir offer a different perspective on the nature of racial profiling in Canada. In doing so, they critique particularly the work of Frances Henry and Carol Tator, David Tanovich, Scot Wortley and other scholars. They also appear to be critiquing qualitative studies that largely rely on the reported experiences of victims who have encountered racial profiling and believe that it exists and is prevalent in policing. These authors believe that the emphasis should be put on the subculture of policing. The importance of this subculture has long been recognized in the sociological literature; and Henry and Tator (2006) acknowledge the of as a crucial element in the dynamic of racial profiling and thus include a substantial chapter on this subject in their book, Racial Profiling in Canada: Challenging the Myth of A Few Bad Apples (92-112). What is often glossed over, however, in studies of police culture is that that culture provides a fertile environment for racism. For example, Harris (2002) argues that the training that police receive encourages stereotypical thinking about particular racial and groups and leads to the belief that skin colour is a valid indicator of a greater propensity to commit crime (11). Through the socialization process, racial biases become fixed ideas and images that are later incorporated into police-department norms. All of this reinforces pre-existing fissures based on race (Harris 2002: 13). Crank (1997: 207) suggests that cultural racism in a police organization is a self-fulfilling phenomenon that can neither be vanquished, perhaps not even contained. We believe that Satzewich and Shaffir (2009) make a contribution to the literature by presenting their own research data based on a study of Hamilton police officers examining how their subculture is being influenced by the demands of policing in multicultural society. They also demonstrate how, today, that police subculture has evolved three discourses with which to deflect allegations of racial profiling. These are the discourse of the intolerance of intolerance, the discourse of multiculturalism, and the discourse of blaming the victim. However, we take issue with some of the points either made or implied in this article. In the first instance, Henry and Tator's work is considered by the authors as far too broad to explain racism or racial profiling either through their concept of democratic racism or through their analysis of the public discourses surrounding it. Their criticism is largely based on the theory's alleged inability to distinguish the intent or motivation of the perpetrators of racism. Henry and Tator (2006) and many other scholars (Tanovich 2006; Chan and Mirchandani 2002; Jiwani, 2002) subscribe to the view that racism/racial profiling is to be judged primarily by its consequences in creating inequality for certain groups. For Satzewich and Shaffir (2009), however, racism/ racial profiling cannot be understood without examining the motivations and intent behind racist actions: It is important to know whether discrimination and social exclusion are driven by intentions and by beliefs that certain groups of people are inferior to others (213). This approach runs directly counter to important court decisions made in human rights law in which racism is determined by consequence to the victim rather than by the intent of the perpetrator, which need not be proved. It is important to remember that, as established by the case law, there is no need to show intent in a case of discrimination since discriminatory effects of the act in question are sufficient (Turenne 2006: 3). The courts have recognized that is unlikely that intent to behave in a racist way and perpetrate racist acts will be successfully proved in court, since very few persons, other than committed bigots, would admit to deliberate racism. …
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».