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Enregistrement W960318797

Are White Collar Criminals Exceptional

2015· article· en· W960318797 sur OpenAlexaff
Jordan Harel

Notice bibliographique

RevueScholarship@Western (Western University) · 2015
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCrime Patterns and Interventions
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésCollarWhite (mutation)CriminologyBusinessPsychologyBiology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Some criminological theories of white collar crime suggest, both implicitly and explicitly, that white-collar criminals are an exceptional type of offender in comparison with other criminals. On the other hand, other scholarship implies the opposite, suggesting that white collar criminals are no different than other types of criminals, and hence, they are believed to be generalist offenders. As such, the following manuscript attempts to examine the following question: are white-collar criminals exceptional?\nThe data utilized for the analysis are an amalgamation of two nationally representative surveys originating in the United States - the 2004 Survey of Inmates in Federal Correctional Facilities and the 2004 Survey of Inmates in State Correctional Facilities. While the total number of cases in the dataset is 18,185, the final analytical sample utilized for the present study is 1,702 respondents. More specifically, it includes 97 white collar criminals, 307 blue collar criminals, and 1,298 thieves.\nThe current project employed a two pronged methodological approach. First, binary logistic regression analyses were conducted comparing white-collar and blue-collar criminals to other types of thieves. The results of these analyses show partial support for both arguments. In line with the idea that white collar criminals are exceptional, the regression models show that they are indeed less likely than thieves to: (1) have a history of property crime; (2) have juvenile delinquency antecedents; (3) heavily use drugs; (4) heavily use cocaine. For many of these outcomes, the analysis also indicates that high education is a key correlate, additional evidence of exceptionalism. Blue collar criminals are exceptional on only two outcomes: (1) juvenile delinquency; (2) heavy drug use.\nIn contrast, the regression analyses also provided support for the idea that white collar criminals are not exceptional, and may well be similar to street criminals such as thieves. More specifically, there is no measurable difference between white collar criminals and thieves on: (1) history of violence; (2) heavy alcohol use; and (3) heavy stimulant use.\nSimilarly, there is no measurable difference between blue collar criminals and thieves on: (1) history of violence; (2) history of property crime; (3) heavy alcohol use; (4) heavy stimulant use; (5) heavy cocaine use.\nIn the second part of the analysis, two-step cluster analysis (a tool for typology-building) was employed in order to create a unique typology of occupational offenders. The results of the clustering revealed four unique groups of occupational offenders. Two groups are in line with the hypothesis of the ‘exceptional white collar criminals’: the ‘hustlers’ (30% of the sample) and the ‘well-to-doers’ (22% of the sample). One group is in line with the ‘white collar criminals as generalists’ hypothesis: these generalists were about 30% of the sample and have high levels of violent and property criminal antecedents and alcohol/substance use. Finally, the fourth group was unexpected given the two main hypotheses of this study: 16% of the sample includes female occupational offenders with high rates of heavy drug use. This last group is labeled ‘female drug users’.\nThe findings of this dissertation are of particular significance to the field of criminology because they advance our knowledge of one of the most understudied and socially deleterious forms of offending within the criminological cannon – white-collar crime. In addition, the results also suggest that “methodology matters” for theory testing. Specifically, regression models are good at detecting average differences between groups (e.g. white collar criminals vs. thieves), while cluster analysis can highlight the presence of sub-groups that would not be visible in a typical regression analysis. At the more theoretical level, this study indicates that white collar criminals are a very heterogeneous group of offenders, and that the general label may well be misleading. Some white collar criminals are indeed exceptional in comparison with thieves or other occupational offenders, but others are generalists for whom white collar criminality is part of a larger criminal history of violence, heavy alcohol and drug use, and other deviant activities.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge 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 enseignants0,113
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,363
Tête enseignante GPT0,421
Écart entre enseignants0,057 · 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

Citations1
Publié2015
Routes d'admission1
Résumé présentoui

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