Bibliographic record
Abstract
Thailand’s crime data collection system is yet to be on par with international standards, resulting in distortedanalysis of actual crime situation and difficulty in formulating crime reduction policy. This research analysescrime data collection system of the United States and the United Kingdom in order to study ways to inaugurate acrime survey and find out the best means of carrying out such project by Rangsit University. This research isconducted qualitatively via in-depth interview of six crime data collection and analysis experts in order torecognise the problems and formulate solutions for crime data collection in Thailand. In addition, a focus groupconsisting of eight crime data collection experts will be formed to determine the procedure involved in theRangsit Crime Survey. The researchers found out that crime data collection system in Thailand, in particular theRoyal Thai Police’s five-group crime data collection, still has shortcomings in terms of redundant and outdatedoffense categorisation, and problems in data collection and dissemination. Crime survey conducted by the Officeof Justice Affairs still has limitations regarding the development of interview forms, data collection, budgetconstraints, and dissemination of data which may be harmful to other state agencies. As for the feasibility ofconducting crime survey by Rangsit University, the experts agree that such survey be conducted in the form ofCrime Fear Poll, the most feasible method which incurs least costs and minimal staff requirements. The name ofthe project will be the “RSU Crime Survey”.
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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 | 0.039 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".