Understanding career criminal kidnapping: a study of offending dynamics, subcultural tolerance and policing in Malaysia
Bibliographic record
Abstract
I subscribe to the notion that criminology needs to seek information about crime from successful criminals. Alohan is a Malaysian, ethnic Chinese, Triad member, businessman and police informer who also kidnaps people for ransom. He is a serious offender who has, so far, escaped conviction for kidnap, which is a capital offence in Malaysia. This thesis seeks to understand the factors underpinning Alohan’s lengthy and apparently successful criminal career but is subject to methodological constraints imposed by ethical and safety concerns. With methods such as participant observation ruled out, the research is based on a series of life history, narrative interviews, conducted with Alohan in a secure location. These are supplemented by semistructured interviews with: officers from Royal Malaysian Customs; officers from the Specialist Police Kidnap Unit of the Royal Malaysian Police, and ethnic Chinese businessmen. Alohan provides an account that can be examined and compared against influential strands of criminological thought in such areas as criminal careers, cultural criminology, subcultural tolerance of deviance and techniques of neutralisation. Alohan’s story reveals the highly culturally specific nature of most influential criminological theorising, which has almost exclusively been generated from a ‘western’ perspective. It uncovers the need for more comparative research in order to fill gaps and correct faulty assumptions that have arisen from the fairly narrow world-view that currently informs the field.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".