Demographic Factors Affecting Employment in Pattani and Songkla Provinces of Thailand
Bibliographic record
Abstract
This study investigated the effects of demographic factors on the employment of people in Pattani and Songkla Provinces of Thailand. The outcome variable is binary employment status (employed or unemployed). The determinant variables are education completion level (none, elementary, secondary, high) and the demographic factors gender, religion (Islam or Other), seven 5-year age groups (25-29 to 54-59), and district. We used data from the 2000 Census of the National Statistical Office. The analysis method involved first stratifying by religion, gender and district and then fitting logistic regression models in each stratum to determine odds ratios for the association between the outcome and the education completion level factor after adjusting for age group, and then combining these odds ratios using meta-analysis to obtain the overall independent association between education completion and unemployment in each province. The results showed that in Songkla province persons who had completed secondary education had no advantage in gaining employment over those who had completed only elementary education. And in Pattani province, those who had completed secondary education had a substantially higher unemployment rate than those with only an elementary education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".