Self-integrity as the Protective Shield for Peer Group Loitering among At-risk Youth
Bibliographic record
Abstract
Loitering among urban youth is often linked with group activities that occur in public places without having any specific purposes. The activity is conducted together as a group among those who share the same values and ideologies. This article analyzes the involvement of prosocial and antisocial activities among 636 young people (423 loiterers and 213 non-loiterers), aged 13 to 25 years in at-risk areas in Kuala Lumpur. The purpose of this study is to compare the prosocial and antisocial behavior of at-risk young people premised by loitering and non-loitering behavior. The researchers compared loiterers and non-loiterers in terms of their levels of self-integrity that may contribute to prosocial and antisocial behaviors. The results showed that at-risk young people who had high levels of self-integrity had lower risk of getting involved in group loitering. On the other hand, the results also indicated that group loitering behavior led to antisocial behavior. In addition, there was no significant difference between loitering and non-loitering behavior in terms of the level of participation in prosocial community activities. This finding supports previous loitering literature which suggests that loitering behavior among young people will lead to risky behaviors. In order to prevent more at-risk young people getting involved in group loitering which may subsequently lead to antisocial behavior, positive youth development programs should focus on boosting young people’s self-integrity. Future studies could focus on the effect of loitering behavior in other at-risk setting in rural area.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".