Bibliographic record
Abstract
This article conducts an in-depth analysis and empirical research on the rising juvenile crime rate in China's transitional society. It first outlines the impact of juvenile crime on social stability and public safety, and reviews preventive measures and suggestions proposed by scholars and research institutions in recent years. The study conducted a questionnaire survey on 1000 juveniles with delinquent behavior in City A, and combined with data from relevant departments, analyzed the types of delinquent behavior, age composition, and family-school relationships. The results show that truancy, staying out late at night, and other general delinquent behaviors are common, and are related to ineffective family supervision. Moreover, gang crimes are prominent and closely related to juveniles' dropping out of school and family conditions. The article concludes by proposing suggestions for addressing family, school, internet, and juvenile crime prevention issues, including strengthening legal education, early intervention, optimizing family and school environments, establishing a juvenile crime risk warning system in smart cities, and establishing an intervention mechanism supported by family-school cooperation. These suggestions aim to reduce the juvenile crime rate, enhance their social adaptability and self-protection awareness, and promote social harmony and progress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".