Influencing External Factors for Small Arms Light Weapon Smuggling at Malaysia-Thailand Border
Bibliographic record
Abstract
End of the Cold War had contributed to the plentitude of firearms within the Southeast Asia region which led to the increase of small arms and light weapon (SALW) smuggling activity. For decades till today, most countries in this region continue to face internal armed conflicts. Malaysia strategic location, situated at the world’s busiest sea lane trades had resulted in rampant cross border crime of SALW smuggling activities. Malaysia’s strict firearms law disallows the possession of SALW without a license. In spite of such strict SALW legislations, these smugglings continues. What are the factors that contribute to the increase of SALW smuggling into Malaysia? The main objective of this article is to scrutinise the external factors that promote the increase of SALW smuggling into Malaysia. This study employs a qualitative method with primary data obtained through preliminary and formal interviews with Malaysian and Thailand security agencies, crime desk journalist, non-governmental organizations, smugglers, former separatist member, former Thai residents, informers and prisoners of SALW related. Whilst secondary data was acquired via credible research. The study found that the national factors and non-national factors have influenced the increase of SALW smuggling into Malaysia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".