UNRAVELING THE DYNAMICS OF JUVENILE DELINQUENCY: A STUDY OF LIVED EXPERIENCES OF YOUTH
Bibliographic record
Abstract
Juvenile delinquency, the participation of minors in illegal activities, remains a critical social issue affecting communities worldwide. The purpose of the study was to assess the factors affecting child delinquency for the students enrolled in elementary grades. For this study, qualitative research design with phenomenological inquiry was adopted. In this regard, the purposive sampling technique was used to select 4-participants who were involved in delinquent acts. Semi-structured interviews were conducted with the participants. The duration of one interview was 30-35 minutes. The audio recording of interviews was transcribed. The data were analyzed through coding and thematic analysis. The result of the study found that there were personal, family and social factors that causing or triggering the delinquent youngsters for socially/legally disapproved acts. Dealing with the causes of child delinquency is important for effectively resolving problem. As there were multiple factors involved in the child delinquency, and multi-sectoral intervention may be adopted by involving the delinquent youngster, their family, their peer group, community, school and environment. The findings of suty provide sinfificant information in reaching the conclusion and thus conctributing the existing knowledge on the issues under consideration in particular context.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".