Politics of alcohol taxation system in Thailand: behaviours of three major alcohol companies from 1992 to 2012
Bibliographic record
Abstract
Sornpaisarn, B., & Kaewmungkun, C. (2014). Politics of alcohol taxation system in Thailand: behaviours of three major alcohol companies from 1992 to 2012. The International Journal Of Alcohol And Drug Research, 3(3), 210 – 218. doi:http://dx.doi.org/10.7895/ijadr.v3i3.155Aim: This study aims to describe the political strategies influencing alcohol taxation in Thailand from 1992 to 2012.Design: This study employs a case study research design, using a mix of qualitative and quantitative data analysis.Setting: Thailand.Findings: Three major companies comprise 92% of the Thai alcohol market, making it an oligopoly market. Ten increases of the alcohol tax rate occurred in Thailand from1992 to 2012, and the Thai government employed differential tax rate policies that favored four of eight beverage categories. These four beverage categories were produced mainly by one of the above-noted companies. Two significant events suggest that the other two companies tried to influence the Thai Prime Minister in 2005 and the Thai Parliament in 2007 to change the alcohol taxation method in order to favor the positioning of their products. As well, evidence revealed that alcohol companies had over-produced their products before three of the eight alcohol taxation increases from 1997 to 2009.Conclusions: Both domestic and international large alcohol companies in Thailand have exerted significant political influence on the Thai alcohol taxation system. This influence is exemplified by the Thai government’s differential alcohol tax rate policy, which favors their products; their ability to stockpile products before taxation increases; and their ability to challenge the Thai taxation system at the national level.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".