Data, Denials and Confusion: The Racial Profiling Debate in Toronto
Bibliographic record
Abstract
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). …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".