Bibliographic record
Abstract
Using a sample of 300 homeless street youths, this study examines differential coercion theory and the role that coercion and the socialpsychological deficits of anger, low self‐control, coercive modeling, coercive ideation, and control imbalances play in the generation of violent crime. Results from cross‐sectional and prospective offending models that examine the individual mediators reveal that coercion has a direct relationship with violent offending as well as a relationship that is mediated by low self‐control, anger, coercive modeling, and coercive ideation. Although control imbalances have a direct relationship with crime, they do not mediate the relationship between coercion and crime. In the cross‐sectional model that contains all the mediators, coercion, low self‐control, anger, coercive modeling, and coercive ideation are associated with crime. In the prospective model that contains all the mediators, only anger, coercive modeling, and coercive ideation remain associated with crime. Results are discussed regarding future theory development and policy implications.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".