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Enregistrement W76894055

A Comparison of Canadian and American Offender Stereotypes

2013· article· en· W76894055 sur OpenAlexaboutno aff
Meredith Allison, Laura Sweeney, Sandy Jung

Notice bibliographique

RevueNorth American journal of psychology · 2013
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCrime Patterns and Interventions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyStereotype (UML)Social psychologyAffect (linguistics)Race (biology)White (mutation)PerceptionCriminologyGender studiesSociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

When asked to think about the characteristics of criminal offenders, what comes to mind? A stereotype is an inflated belief associated with a particular used to justify (rationalize) our conduct in relation to that category (Allport, 1979, p. 191). Research on offender stereotypes has suggested that people do hold stereotypes of offenders (MacLin & MacLin, 2004) and that these stereotypes can affect one's perceptions of defendants (Yarmey, 1993). Thus, it is important to examine stereotypes because such beliefs can affect legal decision-making (Landy & Aronson, 1969). Some researchers have studied which demographic characteristics are associated with the general offender stereotype. For example, participants in Reed and Reed (1973) perceived the typical criminal as an uneducated male who had psychological issues. Madriz (1997) found that the typical criminal was male and Black and/or Hispanic. Some of her participants also described criminals as immigrants. MacLin and Herrera (2006) asked participants to list the first ten things that came to mind when they heard the word criminal. Here, the typical criminal was seen as male. In terms of race, Blacks had the highest ranking (40%), followed by Hispanic (30%), White (20%), and Asian (10%). MacLin and Herrera's (2006) study suggested that the typical criminal was seen as a male, a visible minority, and on average, 25 years old. When it comes to social categories, race has been a major focus in stereotype research. In one study, participants were asked to rank different and the likelihood that they are perpetrated by various racial groups (Gordon, Michels, & Nelson, 1996). The results showed that Blacks were seen as more likely than other racial groups to commit blue-collar crimes, such as aggravated assault, motor vehicle theft, and violent offenses. Whites, in contrast, were seen as more likely to commit white collar crimes such as embezzlement, forgery, and fraud. Similarly, Welch (2007) noted that in the United States, Blacks/African Americans are perceived as more likely to be offenders in general, perpetrators of violent in particular, and that these stereotypes contribute to racial profiling. There is some suggestion that such stereotypes of Blacks hold across cultures. Henry, Hastings, and Freer (1996) surveyed Canadian community members. They found that 37% of participants believed that there is a relationship between racial/ethnic group and the likelihood that a person would be involved in crime. Of the participants who linked race and crime, a majority (61%) believed that the groups most responsible for crime were Jamaicans, other West Indians, and Blacks. Stereotypes regarding gender and crime typically have focused on views of women as victims of crime (Howard, 1984). In terms of female offenders, there have been some studies on women as perpetrators of rape. Specifically, several studies have noted that females are not seen as typical perpetrators of rape, especially the rape of male victims (Smith, Pine, & Hawley, 1988; Struckman-Johnson & Struckman-Johnson, 1992). As a result of such gender stereotypes, female offenders are often treated more leniently than male offenders in cases involving rape (Davies, Pollard, & Archer, 2006; Smith et al., 1988) and robbery (Ahola, 2012). Researchers have focused less on the stereotype concerning the typical offender's age. In the psychology and law realm, what research exists on age and stereotypes has tended to focus on older adults and children as victims and witnesses rather than as offenders (Lachs et al., 2004; Mueller-Johnson & Ceci, 2007; Ross, Dunning, Toglia, & Ceci, 1990). One age group that has received some recent attention in the stereotype literature is that of juvenile offenders. Haergerich, Salerno, and Bottoms (2012) suggested that there may be two subcategories of the juvenile offender stereotype: Superpredator and Wayward Youth. …

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,225
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,074
Tête enseignante GPT0,424
Écart entre enseignants0,351 · 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 tête enseignante, pas un consensus.

Devis d'étudeObservationnel
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

Citations9
Publié2013
Routes d'admission1
Résumé présentoui

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