The Untapped Potential in Our Communities to Assist Youth Engaged in Risky Behaviour.
Bibliographic record
Abstract
By drawing upon what is known about risk factors and protective factors with respect to at-risk youth, the author discusses how communities can become more actively involved and supportive of young people, and thereby work towards a model where the community actively promotes resilience among children and youth. She provides a detailed review of the research around resilience to support her contention that many resources, already present in communities but largely untapped, have the potential to encourage vulnerable young people to avoid developing an aggressive posture towards others, dropping out of school, drifting into a criminal lifestyle, or being victimized. She notes the growing and consistent evidence that poverty, unemployment, abuse, family and school problems correlate to crime, and argues that while one cannot say with any certainty that these factors are the causes of crime, they certainly are the causes of disadvantage. It is the disadvantaged, she states, who are the “most thoroughly processed” by the criminal justice system.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".