Exploring Two Types of Aggressive Behavioural Risk Factors among Illegal Motorcycle Street Racers in Malaysia
Bibliographic record
Abstract
Illegal motorcycle street racing is a threat to civil society – it is a symbol of adolescents’ inner rebellion who channelled their unfulfilled desire through aggressive behaviour on the road, causing significant social and economic impact. Aggressive behaviours have been associated with prohibited substances intake, lack of religious knowledge, problematic family structures, and school failure. In this qualitative study, abductive strategies oriented to phenomenological approaches were employed to assess two types of aggressive behaviour risk factors, which were substance abuse and problematic family structures. In-depth interviews were conducted with thirty people in Penang, Malaysia, who participated in illegal street racing, referred to as Mat Rempits. Their responses were analysed using the NVivo software version 12. The results demonstrate three subthemes to prohibited substances intake: to relieve stress, for personal enjoyment, and for racing purposes, whereby the drugs are taken before races for the riders to be more courageous, aggressive, and agile manoeuvring the motorcycles. Meanwhile, the risk factor of family problems includes divorced and conflicted parents, raised by violence, being neglected, and not being appreciated by the family. Most of the participants stated that growing up with violence caused a psychological impact on their soul, making them stubborn, rude, and aggressive. The results demonstrate the need for a specific intervention programme for the adolescent to reduce their involvement in illegal street racing and aggressive behaviour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".