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Record W1618736104 · doi:10.5539/ass.v11n24p331

Inferior to Non-humans, Lower than Animals, and Worse Than Demons: The Demonization of Red Shirts in Thailand

2015· article· en· W1618736104 on OpenAlexvenueno aff
Siwach Sripokangkul

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyHatredEliteInjusticePoliticsSociologyDemonizationLawPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.030
GPT teacher head0.325
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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