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Enregistrement W2104805308 · doi:10.3138/cjccj.45.3.391

Media Hype, Racial Profiling, and Good Science

2003· article· en· W2104805308 sur OpenAlexvenueaboutno aff
Alan Gold

Notice bibliographique

RevueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2003
Typearticle
Langueen
DomaineSocial Sciences
ThématiquePolicing Practices and Perceptions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRacial profilingProfiling (computer programming)NewspaperPublicityInjusticeRacismPsychologySocial psychologySociologyLawMedia studiesPolitical scienceComputer scienceGender studies

Résumé

récupéré en direct d'OpenAlex

The requirements for good science are basically simple. They comprise a clear understanding of what is being investigated; objective and reliable collection and recording of observations, findings, or data; and rational analysis of those observations, findings, or data in order to draw sound conclusions. The requirements for articles that sell large numbers of newspapers are clearly different, comprising good stories or anecdotes, generally told by sympathetic victims of some injustice or other, accompanied by sweeping generalizations couched in emotional or provocative language. The latter masquerading as the former is what is found in the Toronto Star's articles claiming to have statistically proved racial profiling on the part of the Toronto Police Services. Anecdotal evidence of racial profiling and the fact that there is, in many quarters, a belief in racial profiling are significant and important social realities. But as evidence of the reality of some objective phenomenon to which that label is being attached, such anecdotal evidence is unacceptable. Anecdotal evidence speaks more to beliefs than facts, especially when the anecdotes and beliefs are themselves being widely publicized in the media. There is more than a real possibility of a vicious circle or self-fulfilling prophecy regarding racial profiling, which begins with claims, is fuelled by publicity, and leads to stronger belief and more claims. An even greater possibility of self-generating smoke without real fire exists where the beliefs have spawned a multimillion-, if not billion-dollar industry devoted to the problem. An Internet search on racial profiling returns tens of thousands of American Web pages. The American Civil Liberties Union has made attacking racial profiling one of its top contemporary concerns, filing several lawsuits, preparing numerous publications and presentations, and highlighting the issue in fundraising materials. Federal and state legislation has been passed in the United States regarding the matter. Several leading experts have built careers and produced best-selling books on the topic, as well as signing on as consultants or expert witnesses on the issue. Conferences have been devoted to the topic, and local and state and federal government departments have been created to deal with the issue. Without any recognition of historical or other differences between the United States and Canada, on the topic of racial profiling our longest undefended border is truly undefended; the American literature and experience has been simply imported in its entirety and assumed holus bolus to be applicable to Canada. The existence of numerous anecdotes and widely held beliefs about racial profiling is cause for concern because of the high social costs involved. It is a matter of great importance that a community believes it is being unfairly treated by the police, completely aside from the question of whether that belief is objectively true. How such a situation arose may be less significant than its resolution. Such a problem requires solutions focused on improving communications, building mutual trust, increasing transparency to allay fears, and ensuring proved occasions of impropriety are quickly and sternly dealt with. On the community side, it also requires an appreciation of the potential for self-serving claims that are either intentionally or even unwittingly false. Guilty accused, unsurprisingly, may use every conceivable avenue to escape punishment. The reality of false, self-serving claims of racial bigotry or harassment must be acknowledged by the relevant group. It is most unfair to the police to demand that every claim of professional impropriety be presumed valid. The necessity for demanding evidentiary verification of claims of bigotry or other impropriety must be appreciated by the relevant community, although, of course, the fairness and other aspects of the verification process can be assessed to ensure they meet appropriate standards. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,030
score de la tête « metaresearch » (Gemma)0,073
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,997
Score d'incertitude au seuil0,156

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0300,073
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,004
Études des sciences et des technologies0,0090,080
Communication savante0,0220,022
Science ouverte0,0020,012
Intégrité de la recherche0,0110,014
Charge utile insuffisante (le modèle a refusé de juger)0,0110,002

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.

Tête enseignante Opus0,141
Tête enseignante GPT0,366
Écart entre enseignants0,225 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations18
Publié2003
Routes d'admission2
Résumé présentoui

Explorer davantage

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