Stuck on the Wrong Side of the Tracks: Crime and Neighbourhood Change Across Adulthood
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".