Did Inter-Regional Trade Agreements Bring Mutual Benefits? An Empirical Scheme of Indonesian Commodity Exports in Asean-China Free Trade Area
Bibliographic record
Abstract
The purpose of the study was to determine the effect of ASEAN-China Free Trade Area (ACFTA) on Indonesia's leading export commodities to China, Hong Kong and ASEAN countries. The methodology used is the model used below is based on New Trade Theory, which involves Comparative Advantage determinants including the total size of the country's economy, similarities in economic size, and differences in relative endowment factors. From the intended purpose, the results of this study are that commodities experiencing the largest growth in exports to all countries in 2010 were commodities with codes: 15, 87, 29 and 40. Meanwhile, commodities with code: 87: Vehicles other than railway or tramway rolling-stock, and parts and accessories thereof, and 27: Mineral fuels, mineral oils and products of their distillation; bituminous substances; mineral waxes, were export commodities whose growth was relatively stable, while commodities with code 26: ores, slag and ash, actually declined, and the potential for Indonesian trade cooperation with ASEAN-4 is still wide open. This was marked by an increase in Indonesia's exports to ASEAN-4 in 2010.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".