International Cooperation in Educating and Training Police: Forwarding ASEAN’s Vision 2020 to Combat Non-Traditional Crimes
Bibliographic record
Abstract
This paper draws a detailed description of overall cross-border crimes that Southeast Asian region must be faced when forwarding one common communities on 2020. In order to improving capacity to preventing and combating non-traditional crimes, enhancing international cooperation in education and training for law enforcement agencies is considered as one of the priorities with the Association of Southeast Asian Nations (ASEAN) members. Thus, identifying effective models and professional opportunities in this field between Universities/Academies Police plays an important role in regional strategies to fighting transnational organized crime. To some extent, this study will divide into three main parts. Part one introduces briefly the ASEAN’s challenges and difficulties in dealing with non-traditional security and its forms of crime must be faced in integrated process with the worldwide. Part two gauges two basically current systems to police education and training around the world and its advantages and disadvantages. Finally, part three will discuss how about perspectives for international cooperation in training for law enforcement agencies to combating non-traditional crimes at ASEAN region in the future.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".