Stuck on the Wrong Side of the Tracks: Crime and Neighbourhood Change Across Adulthood
Notice bibliographique
Résumé
Moving from a disadvantaged neighbourhood to one of more affluence has been shown to improve life outcomes. However, not everyone manages to overcome the environmental and social hazards of such neighbourhoods. Success may depend on individual differences such as childhood social behaviour, education, and criminal activity. Crime and neighbourhood disadvantage are highly correlated, but the directional nature of this relationship and its transactional nature throughout life have rarely been examined. Part One of the current investigation examined whether individual characteristics, including childhood social behaviour, education, and criminality, contribute to the perpetuation of socioeconomic immobility across adulthood via neighbourhood disadvantage using a growth curve model. In Part Two, the potential transactional nature of associations between crime and disadvantage over time were examined utilizing a cross-lagged analysis. \nParticipants were drawn from the Concordia Longitudinal Research Project, a prospective, 47-year longitudinal investigation of over 4000 families from neighbourhoods of low socioeconomic status in Québec, Canada. In Part One, Growth curves modeled differences in change in participants’ neighbourhood disadvantage (via census data) over 30 years, from middle-childhood (age 7-12) to middle-adulthood (age 46-57). Predictors included childhood social behaviours and total criminal charges in early adulthood (age 18-28). In Part Two, to examine potential transactions, cross-lagged associations were modeled between neighbourhood disadvantage across four time points (1976, 1986, 1996, 2006). In this model, childhood neighbourhood disadvantage (1976) and aggression were included as predictors and total years of education was included as a mediator. \nPart One results indicated that participants with no criminal charges showed the greatest improvement in neighbourhood over time, whereas those with many charges showed little improvement. Participants with histories of childhood aggression, withdrawal, or lower likeability were also less likely to experience improvements. Results from Part Two indicated that the association between charges and neighbourhood disadvantage was transactional over time and that education may play an important protective role for individuals who grow up in disadvantaged neighbourhoods or for more aggressive children. These findings provide evidence for the importance of criminality in undermining at-risk young adults’ ability to overcome neighbourhood disadvantage, highlighting risk and protective factors that may inform early and long-term intervention and policy.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».