Rejoinder to Satzewich and Shaffir on “Racism versus Professionalism: Claims and Counter-claims about Racial Profiling”
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".