Relationship between Type of Risks and Income of the Rural Households in the Pattani Province of Thailand
Bibliographic record
Abstract
This study examines the relationship between type of risks and income of the rural households in Pattani province, Thailand using the standard multiple regression analysis. A multi-stage sampling technique is employed to select 600 households of 12 districts in the rural Pattani province and a structured questionnaire is used for data collection. Evidences from descriptive analysis show that the type of risks faced by households in rural Pattani province are job loss, reduction of salary, household member died, household members who work have accident, marital problem and infection of crops/livestock. In addition, result from the regression analysis suggests that job loss, household member died and marital problem have significant negative effects on the households’ income. The result suggests that job loss has adverse impact on households’ income. The implication of this is that the living standard of household will continue to deteriorate as large proportion of them could either not find job or lost their jobs. Therefore, an important policy suggestion is that government should formulate a policy that considers the creation of employment especially for the poor households with low-income particularly in the rural area. Also, government should provide an appropriate social security benefits program on which the affected population can rely on in case of problem such as sickness/accident/death of the household members. Concerning the marital problem in the households, an important implication to the policy maker is to formulate a policy or design strategy development principles of holistic family.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".