Notice bibliographique
Résumé
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.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,014 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,005 | 0,009 |
| Intégrité de la recherche | 0,011 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».