The Communities of Lottery Sellers: Socio-economic and Cultural Changes in Isan, Thailand
Bibliographic record
Abstract
The qualitative research was carried out in Amphoe Wang Saphung, Loei Province; Amphoe Kranuan, Khon Kaen Province; and Amphoe Kaedam, Maha Sarakham Province between October 2012-April 2013 using a survey, observation, interview, focus group discussion and workshop. The 110 sample were divided into groups of 30 key, 50 casual, and 30 general informants respectively. The analysis was based on the research objectives using a triangulation technique and done descriptively. Historically, the people who lived at Amphoe Wang Saphung began to sell lotteries in 1984. Twelve years later, such part-time job spread to Amphoe Kranuan, Khon Kaen Province and Amphoe Kaedam, Maha Sarakham Province. Currently, the lottery sellers at the three villages chose to sell lotteries in Bangkok, tourist areas, such as Rayong and Nakhon Ratchasima Provinces. Some decided to sell theirs in their own Province. Each month, they left their homes trice: the second and the fourth weeks. They often went in groups using pickup trucks, vans or buses. If they worked in their own Province, they often used motorcycles. The problems encountered included car accidents, cheats, arrests due to selling lotteries overprice, and school children skipping classes or drung-addicted.For socio-economic and cultural changes, the lottery sellers had improved their lives materially due to their income earning from selling lotteries. They could afford what they needed, such as land, home appliances, and building jobs in their villages. They had new friends and established social network. Young people chose to marry with outsiders. The people set up lottery selling groups of volunteers. They took health care to political representatives, and village leadership.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".