Types, Problems and Their Causes, and Solutions to the Offences against the Environmental Laws by Probationers in Maha Sarakham Province
Bibliographic record
Abstract
This study aimed to explore types, problems and their causes, and solutions to the offences against the environmental laws of probationers in Maha Sarakham Province. The study comprised 2 phases: Phase 1 was a study of types of the offences against the environmental laws: and phase 2 was an interview with 25 people directly dealing with the probationers including judges, public prosecutors, probation officers, lawyers and 20 probationers. The findings revealed that the offence types against the environmental laws were both criminal cases and civil suit cases which caused impacts on the environment and natural resources. Most problems were caused from offenders’s lack of knowledge, understanding, and awareness of the environmental laws, no participation in the environmental conservation, unemployment, drug addiction, moral decline, incorrect values, broken families, economy recession, poverty, social inequality, and communication technology problems etc. Hence, the solutions to solve these problems are educating the people about the related laws starting from a family, a school, a training institution both in government and private agencies: building a good sense toward the society and environment: and building the habit of participation in maintaining the social regulations.
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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 | 0.000 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".