The Social and Developmental Antecedents of Legal Cynicism
Bibliographic record
Abstract
Objectives: This study explores the social and developmental antecedents of legal cynicism. This study comprises a range of indicators organized into four domains—bonds to institutions, predispositions, experiences, and delinquent involvement—that bear on theoretically plausible mechanisms involved in the development of legal cynicism. Methods: This study examines four pathways to legal cynicism using data from two waves of the Zurich Project on the Social Development of Children and Youths ( N = 1,226). Ordinary least squares (OLS) procedures are used to regress legal cynicism at t 2 (age 15) on social and psychological characteristics measured at t 1 (age 13), and retrospective variables measured at t 2 . Baseline legal cynicism was included as a covariate in all models. Results: The results show that self-reported delinquency is the strongest predictor of legal cynicism. There is also evidence that alienation from society, negative experiences with police, and association with deviant peers can foster legal cynicism. Conclusions: This study shows that legal cynicism is to a small extent the result of alienation from social institutions and negative experiences with the police. To a much larger degree, legal cynicism seems to represent a cognitive neutralization technique used to justify one’s previous self-reported delinquency.
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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 | 0.011 |
| 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 | 0.002 |
| 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".