Narcotics-Free: Strong Community Development Models by Community Participation in the Lower Isan Region
Bibliographic record
Abstract
In the past, most Thai people lived in rural areas more than they did in towns or cities. It was only recently that a lot of people began to move to towns or cities looking for jobs making up about 30 percent of total population of Thailand Wherever they lived traditions (Buddhist and alike) and practices for or in everyday life. There were and still are differences from place to place. Drug problems, especially hard drugs were not known in most areas. As the country moved into the development age, the population, to some extent, had experienced drug problems and severe consequences. Families, communities and authority had come together in order to solve such problems. The problems found included running after material wealth particularly among young and working people. As a part of looking for happiness, many were lured in using narcotics or hard drugs. The communities tried hard to get back to their previous drug-free position by solving drug problems and developed themselves by drawing financial and academic support from concerned government and private sectors during 1992-2001. They also received the Mother of the Earth Award.For strong community development models with narcotics-free by community participation in the Lower Isan Region, the study found that cultural aspects were drawn into making strong communities. They included way of life, kinship systems, beliefs, rituals, traditions and thoughts, beliefs, rituals, traditions, and thoughts. The strong communities, based on cultural aspects could be used for drug-problem solving and community development.
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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 | 0.005 | 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 | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".