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Record W621443909 · doi:10.5509/20168917

Professionals and Soldiers: Measuring Professionalism in the Thai Military

2016· article· en· W621443909 on OpenAlexvenueno aff
Punchada Sirivunnabood, Jacob I. Ricks

Bibliographic record

VenuePacific Affairs · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceMilitary personnelPsychologyMedical educationMedicineLaw

Abstract

fetched live from OpenAlex

Thailand's military has recently reclaimed its role as the central pillar of Thai politics. This raises an enduring question in civil-military relations: why do people with guns choose to obey those without guns? One of the most prominent theories in both academic and policy circles is Samuel Huntington's argument that professional militaries do not become involved in politics. We engage this premise in the Thai context. Utilizing data from a new and unique survey of 569 Thai military officers as well as results from focus groups and interviews with military officers, we evaluate the attitudes of Thai servicemen and develop a test of Huntington's hypothesis. We demonstrate that increasing levels of professionalism are generally poor predictors as to whether or not a Thai military officer prefers an apolitical military. Indeed, our research suggests that higher levels of professionalism as described by Huntington may run counter to civilian control of the military. These findings provide a number of contributions. First, the survey allows us to operationalize and measure professionalism at the individual level. Second, using these measures we are able to empirically test Huntington's hypothesis that more professional soldiers should prefer to remain apolitical. Finally, we provide an uncommon glimpse at the opinions of Thai military officers regarding military interventions, adding to the relatively sparse body of literature on factors internal to the Thai military which push officers toward politics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.322
Teacher spread0.271 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations9
Published2016
Admission routes1
Has abstractyes

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