Doing Better with “Bad Kids”: Explaining the Policy-Research Gap with Conduct Disorder in Canada
Bibliographic record
Abstract
Conduct disorder (severe and persistent antisocial behaviour in children and youth) is an important community mental health problem in Canada and has been the focus of considerable recent public policy debate. Good research evidence is available on effective (and ineffective) interventions for conduct disorder. Paradoxically, however, relatively little of the research evidence is incorporated into policy decision-making. There is a policy-research gap. An example (Hamilton, Ontario) is used to illustrate this gap. The gap is then explained using a framework for health policy analysis that incorporates values, institutional structures, and information. Values and institutional structures greatly outweigh research evidence in influencing current Canadian policy-making for the problem of conduct disorder. Possibilities for improving the situation are suggested. The youth who sprang his girlfriend from Arrell Youth (secure custody) Centre has a record many career criminals would envy. Unfortunately for society, he just turned 15 and has discovered an interest in illegal handguns. “This is a one-kid crime wave,” said a Hamilton-Wentworth police officer who spoke on condition of anonymity. “He's not like other child criminals—not even close. He's a bad kid.” That kid is in police custody today (Herron, 1995).
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".