Residential stability among adolescents in public housing: a risk factor for delinquent and violent behaviour?
Bibliographic record
Abstract
Introduction The private housing market exposes working tenants to great instability. Private housing residents are shuttled around by rezoning, redevelopment, renewal, evictions, and slumlord neglect. Residents flee intolerable sanitary conditions, rats that bite their children, and other domestic hazards. In this context public housing remains an oasis of stability not simply for the dependent single-parent families, but also for the poor and working households who at the very least can rely on their housing status when all else is in flux. (Venkatesh, 1997, p 36) Renewed interest in understanding the links between public housing and crime is evidenced by recent research in Australia (Weatherburn et al, 1999), Canada (DeKeseredy et al, 2003), the UK (Bottoms and Wiles, 1986; Bottoms et al, 1992; Flint, 2002), and the US (Popkin et al, 2000; Santiago et al, 2002; Ireland et al, 2003). The general consensus is that areas where public housing is located have higher rates of official crime or reported victimisation. The opinion that public housing is crime-ridden is reflected in several local and federal initiatives in the US that are directed at controlling crime and drugs in and around public housing, including local police actions in public housing (Popkin et al, 1999; Barbrey, 2004), the Public Housing Drug Elimination Program (for example, Popkin et al, 1995), and the One-Strike and You’re Out initiative (Dzubow, 1996; Hellegers, 1999), as well as efforts to disburse assisted housing out of the most impoverished areas of the city via HOPE VI (Naparstek et al, 2000; Popkin, 2003) and the Moving to Opportunity Demonstration Project (Briggs, 1997; Popkin et al, 2002). However, Ireland et al (2003) found that self-reported involvement in property and violent crime among adolescents residing in public housing in Rochester, New York and Pittsburgh, Pennsylvania was not statistically significantly higher than among adolescents not in public housing. Their cross-sectional study did, however, find that in large developments or high-rise units in Pittsburgh the level of selfreported violence was quite high, particularly during late adolescence. In this chapter we attempt to ascertain whether living in public housing for a relatively long period of time is more behaviourally detrimental than living in public housing for a relatively short period of time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".