A Cooperative Study of Self-Esteem, Leadership and Resilience amongst Illegal Motorbike Racers and Normal Adolescents in Malaysia
Bibliographic record
Abstract
Understanding self-esteem, leadership and resilience among at risk youth who are involved in illegal motorbike racing is a crucial issue prior to starting any intervention programs. It may provide an indication of their profile in order to change this negative behavior. This study aimed in examining the relationship between self-esteem, leadership and resilience among illegal motorbike racers and its comparison with normal adolescents. The study employed survey research involving the administration of three standardized psychological tests namely the Rosenberg Self-Esteem Scale (RSE), the adapted Multifactor Leadership Questionnaire (MLQ) and the Resilience Questionnaire (RQ). A total of 140 respondents participated in this study. Data were analyzed using Pearson correlation and t-test analysis. Results showed that there were significant correlations between self-esteem, leadership and resilience dimensions among normal adolescents. However there were no significant correlations between self-esteem, leadership and resilience dimensions among illegal motorbike racers. In addition, there were significant differences of self-esteem, leadership and resilience between normal adolescents and illegal motorbike racers. In conclusion, normal adolescents had higher self-esteem and leadership and they showed higher resilience while illegal motorbike racers showed lower self-esteem and leadership and in turn they were less resilient. This implied the need for continuous intervention programs in order to empower at risk youth. It was recommended that future studies explore other variables such as family and school variables that can influence resilience.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".