Bibliographic record
Abstract
The last decade has seen a vigorous debate in Canada and elsewhere about the existence of racial and religious profiling. Although not all have been convinced that discriminatory profiling exists or that it is a problem, many have. Much of the work that has been done on discriminatory profiling has focused on rights violations and not remedies. In 2001, an American commentator argued that while much important legal work has been done to challenge discriminatory profiling, “the same legal work that has helped to create an opportunity for change has distracted lawyers, advocates, commentators and police from focusing on the creation of effective remedies for racial profiling.” In Canada as well, not enough attention has been paid to what remedies are necessary to compensate for and prevent discriminatory profiling. The question of remedies should be broadly conceived. Only an impoverished vision of remedies would conceive of court-ordered remedies — exclusion of evidence, damages, declarations and injunctions — as the main form of remedy for discriminatory profiling. Lawyers in particular must broaden their horizons beyond remedies ordered by courts to include remedies that may be devised by administrative bodies and tribunals, legislatures and police and security agencies themselves.A remedy broadly conceived would include not only the fashioning of some act of compensation or reparation for past acts of profiling, but also a variety of systemic measures designed to prevent or minimize the risk of profiling in the future. It could also include systemic reform with respect to review practices or whole areas of law enforcement that would either reduce the risk of discriminatory profiling or provide better remedies for when it occurs. In the first part of this chapter, I will elaborate on my argument about the importance of paying more attention to questions of remedial choice by outlining a few cautionary tales about the failure to devise effective and meaningful remedies. Excessive remedial claims may help contribute to a failure to recognize rights, while minimal remedies may result in successful litigation producing only hollow victories. The difficulties of obtaining effective remedies should not be underestimated and the process of searching for effective remedies may be one of trial and error. In the second part of this chapter, I will outline some of the judicial remedies that could be employed against discriminatory profiling and in the third and fourth parts I will examine administrative and legislative remedies respectively. My approach will not be to suggest that any particular remedy should be preferred but to broaden the remedial imagination. In the end, remedial choice is a matter to be determined by clients and communal deliberation. In addition, the various judicial, administrative and legislative remedial strategies that I outline should not be seen as mutually exclusive. Pervasive problems such as discriminatory profiling will require multiple remedial strategies and multiple remedies. At the same time, thought should be given to the strengths and weaknesses of each remedy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".