Bibliographic record
Abstract
This commentary was prompted by the recent debate in this journal (CJCCJ 45.3, July 2003) exploring allegations of on the part of the Metro Toronto Police Service. My intention is to deal with the issue more generally, rather than to comment specifically on the situation in Toronto. I share with many commentators the concern about racial profiling; however, this mutual concern has little meaning when the term has such varied connotations. My preference is for a more narrow usage, as broader definitions include law enforcement practices that, arguably, are legitimate. Therefore, racial as defined here, is a form of bias whereby citizens are stopped, questioned, searched, or even arrested on the basis of their minority status per se, rather than due to a demonstrated, elevated risk of lawbreaking. Several rigorous studies undertaken in the United States provide evidence of such racial profiling. For example, John Lamberth of Temple University (cited in Harris 1999) sent out teams of observers to the New Jersey Turnpike and, based on observations of over 42,000 vehicles, found that black and white drivers violated traffic laws at virtually identical rates. However, police records indicated that 35% of those stopped and 73% of those stopped and arrested were black, while only 13.5% of the cars on the road had a black driver or passenger. Lamberth concluded that would appear that the of the occupants and/or drivers of the cars is a decisive factor [in the number of stops of blacks] or a factor with great explanatory power (Harris 1999: 198). Lamberth's study illustrates what I consider to be the two main elements of racial profiling: (1) members of a visible minority group have a significantly elevated likelihood of being subject to some form of police action, and (2) the more aggressive targeting of that group is due to the group's visible minority status, rather than to behavioural differences that might warrant a higher level of police scrutiny. Alan Gold, one of Canada's most prominent barristers, adopts a precise definition and one that is consistent with the above: racial is thus (i.e., identification of target criminals) based upon one characteristic: race. If is an attempt to identify previously undetected criminals based upon the single factor of race (2003: 394). According to the above definitions, racial profiling is a consistent tendency on the part of members of a police service to target a group in the absence of credible evidence that might warrant such targeting. Unfortunately, definitions of this phenomenon are often so broad that they include reasonable and legitimate police practices. It is worth contrasting the definition provided by Scot Wortley and Julian Tanner with those advanced above. They write, In the criminological literature, is said to exist when the members of certain or ethnic groups become subject to greater levels of criminal justice surveillance than others. Racial profiling, therefore, is typically defined as a facial disparity in police stop and search practices, differences in customs searches at airports and border crossings, increased police patrols in facial minority neighbourhoods and undercover activities, or sting operations that selectively target particular ethnic groups. (2003:369) While Wortley and Tanner are careful to attribute this definition to the criminological literature, they appear to adopt it at various points in their discussion. My concern is that this definition fails to distinguish between law enforcement practices that are based on pure bigotry and those that may be entirely reasonable as a result of systematic analyses of crime patterns, intelligence work, and information obtained from the community. It is legitimate for a police service to deploy additional personnel in neighbourhoods experiencing high levels of illegal activity, regardless of whether or not the residents tend to be members of visible minority groups. …
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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 | 0.002 |
| 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".