Bibliographic record
Abstract
Political reform movements have grown up as part of democratic transition in many societies. Liberal political reformers typically seek to change legal and institutional mechanisms in order to "clean up" the irrationality and corruption of the regime. This essay uses the Thai case to critically examine these issues by interrogating the central role that the discourse of vote buying plays not just in Thai politics, but in the project of political reform itself. Indeed, in the 1990s vote buying turned from being one of many campaign tactics into the guiding metaphor of the "political disease" not simply of elections, but of Thai society in general. Rather than seeing vote buying as a coherent "thing," the essay will examine how this varied practice of electoral fraud has been reduced into a key category of Thai politics - "Vote-Buying." By demonstrating how vote buying is tied to its opposite - bourgeois democracy - one can better examine how both vote buying and democracy are co-produced in various networks of power relations. The essay examines key discourses to show how the concepts of law, "good and able leaders," gangsters, the middle class, civil society, and village life are central in defining both vote buying and democracy in popular media and thus the popular imagination. Vote buying is produced in specific relations between political and economic power, urban and rural power, and official and unofficial power; to fight it one needs to challenge the dynamics of these relations.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".