The Trend of Crimes Committed by Lao Immigrants in Thailand After the Inauguration of ASEAN Community
Bibliographic record
Abstract
This research is directly relevant to the ASEAN Political - Security Community (APSC). One of the fundamental visions is particularly associated with combatting transnational crime and international challenges effectively in a timely manner. This study has three objectives as follows; (1) to explore causes and current situation of crime resulting from cross-border migration from Laos to Thailand due to the establishment of ASEAN community, (2) to deeply analyse immigration and crime trends across Thai-Lao borders, and (3) to propose approaches to preventing the immigration from Laos to Thailand for criminal purposes. In-depth interviews were conducted to gather crucial information from key informants, followed by two focus group discussions. However, the trend of crimes committed by Lao immigrants in Thailand after the inauguration of ASEAN community is inconsistent with the hypothesis and public fear of general people who believed that checkpoints for border trade for commodity flows and labour transfers would worsen the issues of drugs and illegal immigration. Research findings indicate that drug-related crime and illegal immigration issues tend to decrease because the opening of ASEAN community allows the Thai and Lao governments to strengthen their international relations. It can be stated that warm diplomatic relations between countries are the main factor contributing to successful prevention for transnational crime.
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 | 0.000 | 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 | 0.000 | 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".