Bibliographic record
Abstract
This paper presents a study of household gambling consumption in Thailand in 2011. We investigate the nonlinear relationships between this behavior and household alcohol expenditure, household gambling expenditure, and demographic factors. We use Treenet to analyze datasets drawn from a socio-economic survey of 42,083 Thai households conducted in 2011. The results show that the five most significant variables in order of importance for predicting the likelihood of household gambling consumption are household income, household region, work status of the household head, religion of the household head, and age of the household head. In summary, the Treenet results suggest that the likelihood of gambling consumption was higher for households with an income of more than 25,000 Bahts per year, a location in the North, a Buddhist head of household, a head with active work status, a head between 35 and 55 years old, with household expenditure spent on alcohol consumed at home of more than 500 Bahts, with household expenditure spent on tobacco of more than 100 Bahts, and a head of household with less 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".