Labeling effects of initial juvenile justice system processing decision on youth interpersonal ties<sup>*</sup>
Bibliographic record
Abstract
Abstract The juvenile justice system can process youth in myriad ways. Youth who are formally processed, relative to being informally processed, may experience more public and harsh sanctions that label youth more negatively as “deviant.” Drawing on labeling theory, the current study evaluates the relative effect of formal justice system processing on the interpersonal dynamics of youth peer networks. Using data from the Crossroads Study, a multisite longitudinal sample of first‐time adolescent offenders, the current study applies augmented inverse probability weighting and generalized mixed‐effects models to estimate the effects of formal processing on friendship selection processes of homophily and withdrawal and considers whether these effects vary by race and ethnicity. Consistent with expectations of homophily, formally processed youth acquire more new deviant peers and fewer nondeviant peers during the 3 years after their initial processing decision compared with informally processed youth. The findings suggest no differences exist across processing types in withdrawal from friends. These effects were consistent across racial and ethnic groups. Ultimately, this study explores the dynamic interpersonal mechanisms associated with labeling theory and offers additional insight into the negative effects of formal processing.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".