Introduction to the Special Issue: Prevention Science and Youth Conduct Problems: Development, Prevention, and Treatment
Bibliographic record
Abstract
This introductory article describes this Special Issue entitled Prevention Science and Youth Conduct Problems: Development, Prevention, and Treatment that is being offered in recognition of the research and scientific contributions of Dr. Robert J. McMahon. This Special Issue includes a collection of 15 original empirical research articles, systematic reviews, meta-analyses, and theoretical pieces spanning three themes consistent with Dr. McMahon's program of research: (1) risk and protective factors in the development and maintenance of youth conduct problems; (2) family based preventive and treatment interventions for youth conduct problems; and (3) multicomponent preventive and treatment interventions for youth conduct problems. Following these articles, this Special Issue contains two commentaries from experts in the fields of youth conduct problems and prevention science, as well as a reflection from Dr. McMahon. Our introduction provides a brief synopsis of each article contained in the Special Issue, identifying how these works reflect upon and were inspired by Dr. McMahon's research legacy and how they advance understanding of conduct problems. We close this introduction with thoughts regarding future research directions that will extend Dr. McMahon's impressive impact on the field of youth conduct problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.051 | 0.023 |
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".