Juvenile Delinquency in Five High Schools in Shenyang, China: \nAn Empirical Analysis under an Integrated Model
Bibliographic record
Abstract
Youth crime has been increasing rapidly since the Economy Reform and Open-door Policy in 1979 and become a serious social problem in China. Researches on explanations of juvenile delinquency, however, are relatively limited, while a number of scholars in western countries have developed delicate theoretical models to explore this problem. General strain, differential association, and social bond theory are employed in the current study to test if western theories can be applied to a different social context and to empirically explain the causes of youth crime in China. An integrated model is addressed through a self-reported survey with 385 respondents. The respondents are high school students in the city of Shenyang, aged from 16 to 18. Data from the questionnaire survey suggests that these three theories could explain Chinese youth crime. Two separate Ordinary Least Squares (OLS) models are built for analyzing delinquency of males and females. Predictors related to strain and differential association theory are directly associated with youth crime, while weak social bonds have indirect impacts on juvenile delinquency. Males and females are influenced by different factors when they are involved in delinquency. The thesis concludes with a discussion of establishing a theoretical integrated model for Chinese adolescence and provides policy implications for protection programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".