Strategies on Movement for Welfare Provision Andemployment Networking with the Principles of Solidarity of informal Female Worker in Loei Province
Bibliographic record
Abstract
This study aimed to develop strategies for building welfare and career development networks for informal female workers in Loei Province, Thailand, based on the Buddhist principle of Samakkhi Dhamma (unity). Many women in the informal sector face problems such as low welfare coverage, financial insecurity, lack of job skills, poor access to legal protection, and weak community cooperation. A mixed-methods research design was used. Quantitative data were collected through questionnaires from 372 participants, and qualitative data were gathered from focus group discussions with 30 women. The findings revealed six main areas for improvement: expanding welfare coverage, easing financial access, improving communication about benefits, raising service quality, supporting healthcare and compensation, and increasing social security participation.These strategies were designed as part of a non-formal, community-based education model grounded in local values. The Buddhist teaching of Samakkhi Dhamma was found to play a key role in encouraging cooperation, unity, and shared responsibility. The study contributes to the field of educational development by showing how cultural beliefs can enhance workforce learning and support social equity. However, the study was limited to one province, and broader national policies were not examined. Further studies are recommended to test the strategies in other regions and explore how religious and cultural values can be used in designing community education and welfare systems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".