A Response to Hier and Walby's Article: Competing Analytical Paradigms in the Sociological Study of Racism in Canada
Notice bibliographique
Résumé
Hier and Walby's 2006 article Competing Analytical Paradigms, published in the last issue of this journal (vol. 38, no. 1), presents a provocative analysis in which two different paradigms used in the study of racism in Canada are identified. I feel compelled to respond to this piece, as I was the first author cited as an example of the paradigm of cultural recognition, which privileges the subjective experiences of human beings. On the whole, the article is interesting and brings attention to what is indeed an important issue in the study of racism in Canada. However, as some important points relevant to the analysis are missing, I feel it is an incomplete examination of this admittedly complex subject. In the first instance, I do not dispute the existence of the two paradigms identified by Hier and Walby, but I would draw some different conclusions based on the research that defines them. These paradigms, which follow the work of Fraser (2003), are the folk paradigms of cultural recognition that identify patterns of inequality as a function of culture and patterns of representation. This differs from the redistribution paradigm, which focusses on the unequal social redistribution of the resources of society. When applied to the study of racism, Hier and Walby suggest that the redistribution model measures the influence of skin colour or other markers of ethno-racial categorization in relation to other social indicators on degrees of social integration ... (90). Whereas ethnicity/race is only one of a variety of variables that influence integration, the cultural recognition model identifies them as the dominant, or primary, influence. It should be noted that these paradigms are themselves integrally related to a much older divide in the social sciences in general, and in sociology in particular. While the subject matter of the Hier and Walby article is the role that these paradigmatic differences play in the study of racism in Canadian society, these distinctions in fact reflect the long standing and contested problem of objectivity and subjectivity, which is at the basis of the more profound issue of value-free science. Related to these basic philosophical or epistemological themes in social science is the question of appropriate methodologies to be used in the study of human social behaviour. I do not think the ideas of value-free science nor the questions surrounding objectivity in research need to be re-visited here; we should all be familiar with them. But I must emphasize that these concerns are often raised in the study of racism, especially by critics who dismiss the so-called soft or largely qualitative research methods that rely on subjective experience. These criticisms are especially crucial in racism studies, because they give rise to the ubiquitous response to what my colleague and I have called the Discourse of Denial (Henry and Tator 2006; Tator and Henry 2006). They lead to the dismissal or minimization of the existence of racism in our society. Such critics often raise the demand for quantification (as in show me the numbers), a strategy that unfortunately guides so much of public policy. This has serious and often negative consequences for enacting appropriate legislation and other public policies designed to increase societal equality and social justice. Thus it is not surprising that many of the studies cited by Hier and Walby as examples of the cultural recognition paradigm largely, but not exclusively, rely on qualitative narrative methodologies and the subjective testimonials of people who are aggrieved, hurt, and disadvantaged by racism. Even such cited studies as that of Ruck and Wortley (2002), who use fairly large numerical samples, still rely on the subjective answers given by their respondents. On the other hand, those identified with the category of social redistribution rely primarily on census or census type data. What then is the message to sociologists and other social scientists when the analysis of census-type data concludes that racial minorities, especially in the second generation, do not experience more disadvantage than others? …
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,000 | 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 ».