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Record W2765716019 · doi:10.5539/ass.v13n11p152

The Soldiers Welfare: The Military Keynesianism Perspective for The Indonesian Armed Forces’s Professionalism

2017· article· en· W2765716019 on OpenAlexvenueno aff
Muradi Muradi

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutionWelfareIndonesianContext (archaeology)Political scienceGovernment (linguistics)SovereigntyPublic administrationEconomic JusticeDemocracyChristian ministryWelfare stateLaw

Abstract

fetched live from OpenAlex

Building professional soldiers into the wishes and commitments of many countries, this is due to the strengthening of threats that are not only traditional threats, but also non-traditional threats. This situation confirms that the presence of professional soldiers will ensure the sovereignty of the state, because the military institution can focus on its duties and functions on the defense of the country. In this context, the Indonesian Armed Forces (Tentara Nasional Indonesia—TNI) is also faced with situations that place TNI institutions to become professionals, relying on democratic civilian government through the defense ministry with an emphasis on improving the welfare of soldiers simultaneously with efforts to modernize Indonesia's defense system. Because the Military-Keynesianism approach believes that improving the welfare of the army is part of the consequences of increasing defense budgets. The paper argues that the increase in defense budget will be correlated with the welfare of the army, although the policy is not directly for the welfare of the army. The article also argues that the increase in the defense budget should improve the TNI foundation as an ideal institution by emphasizing the welfare of soldiers.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.999

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 studies0.0220.004
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.316
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations1
Published2017
Admission routes1
Has abstractyes

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