Countertrade Mechanism of Global Arms Trade: Case Study of Indonesia
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".