Inferior to Non-humans, Lower than Animals, and Worse Than Demons: The Demonization of Red Shirts in Thailand
Bibliographic record
Abstract
Attempts to demonize and dehumanize the Red Shirts, the largest pro-democracy group in Thailand, have been evident since the 2006 coup d’état that deposed Thaksin Shinawatra as prime minister. In this article, the author discusses the origins of the Red Shirts, and argues that the formation of the Red Shirts was in reaction to unbearable injustice in Thai society. Applying Giorgio Agamben’s theory of bare life and qualified life, the author shows that the Red Shirts have been stripped of their political life and status in Thai society. The author discusses the hate speech and brutal tactics used against the Red Shirts both before and after the massacre of 2010 in which over 100 people died, which occurred during Red Shirt protests against the Democrat-backed Abhisit Vejjajiva government. The elite and middle classes, as well as the Army, incite hatred against the Red Shirts through the use of propaganda that depicts them as disgusting beings in order to justify their eradication. However, contrary to popular belief among the elite and middle class, the author argues that Red Shirts are rational in their thinking towards democracy, and are not primarily motivated or controlled by money as voters. Above all, the author concludes that the Red Shirts have been treated unjustly by the elected government they supported between 2011 and 2014.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".