The Prediction of Delinquency among Immigrant and Non-Immigrant Youth
Bibliographic record
Abstract
Using a cross-sectional data set of 837 male adolescents (346 local Germans, 375 ethnic German immigrants from the countries of the Former Soviet Union (average length of residence = 7.6 years), 52 first-generation (= foreign born, average length of residence = 9.2 years) and 64 second-generation immigrants (= born in Germany) from other origins such as Turkey, Former Yugoslavia or Afghanistan) aged 15.4 years, this article brings together acculturation research with research on behavior problems in non-immigrant youth. Male immigrants reported higher numbers of delinquent acts pursued in the last 12 months than local boys. First-generation immigrants from mostly Muslim backgrounds show the highest amount of problem behavior. In spite of these differences, comparisons of structural equation models show that the prediction of delinquency is about the same (delinquent beliefs and friends, clique membership, parental monitoring, and language problems) for all four groups. Applying stepwise regression analyses, most of the culture-related variance in boys' delinquency is explained by the same set of predictors with delinquent beliefs and parental violence being the most powerful markers for the differences between the groups. The discussion sheds light on the living situation of immigrant youth in Germany and why delinquent beliefs may be of central importance to understanding the differences between local and immigrant youth.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".