Bibliographic record
Abstract
Individuals who have committed a number of crimes over their lifetimes have had complex, multi-faceted life experiences often characterized by extreme disadvantage and victimization.Those who are formally designated as "high-risk" by the Canadian criminal justice system often have a record of violent or sexual crimes.As a result, they are usually subject to additional monitoring in the community after completing a prison sentence.Pathways to Ruin disentangles the numerous elements and pathways that lead to high rates of reoffending by focusing on developmental periods of childhood, adolescence, and adulthood.The book uses a case-study approach to consider individuals' entire crime pathway by examining the circumstances and factors that contribute to assumptions or official designations of "high-risk" behaviour.Erin Gibbs Van Brunschot and Tamara Humphrey overhaul society's popular crime narratives and instead draw on sociological and criminological perspectives to identify historical, social, and personal contexts that appear to increase the likelihood of reoffending.They also consider how negative life experiences may be addressed to circumvent trajectories of serious offending.Reducing the social distance that the "law-abiding" public may feel towards marginalized groups, Pathways to Ruin details how legal systems could better serve these individuals, and acknowledges the many missed opportunities for compassion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.886 | 0.784 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".