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Record W2989846259 · doi:10.5430/ijfr.v11n1p307

Countertrade Mechanism of Global Arms Trade: Case Study of Indonesia

2019· article· en· W2989846259 on OpenAlexvenueno aff
Zainal Arifin, Agus Suman, Moh. Khusaini

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInternational tradeProcurementBusinessPaymentDefense industryHonorPosition (finance)Function (biology)CommerceEconomicsMarketingFinance

Abstract

fetched live from OpenAlex

A strong national defense system serve to maintain the nation's honor such as creating peace, security, and sovereignty and becomes an effective instrument for bargaining position in relations between nations so that it has broad impacts, including impacts on economic aspects. The research approach used is a qualitative approach. This type of research is a systematic review research. Likewise, the defense industry of a country reflects the economic strength of its country, because in carrying out the defense function, the defense industry has a very important role, including in holding the national Main Tool of the Armament System. However, now not all defense equipment can be produced by the domestic defense industry, so some defense equipment must still be held in cooperation with foreign countries. Law No. 16 of 2012 concerning the Defense Industry mandates that procurement of defense equipment from abroad be permitted if it fulfills several requirements, including trade returns, local content, and offset. Counter trade is one of the mechanisms of cooperation that has been carried out by Indonesia. This mechanism allows reciprocal trade between two countries by buying goods from abroad with payment in the form of goods worth the goods imported. So it is important to analyze the potential for economic improvement for Indonesia that is generated by the mechanism of trade defense equipment from abroad, so that in conducting cooperation, Indonesia can benefit both in the short and long term.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.094
GPT teacher head0.371
Teacher spread0.277 · 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 designObservational
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

Citations5
Published2019
Admission routes1
Has abstractyes

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