STRATEGI AKSES PASAR KERJASAMA PERDAGANGAN INDONESIA KANADA DALAM KERANGKA COMPREHENSIVE ECONOMIC PARTNERSHIP AGREEEMENT (CEPA)
Bibliographic record
Abstract
Indonesia does not yet have a free trade agreement with Canada, as other ASEAN countries have done. Meanwhile, Indonesia-Canada trade activities have been running where Canada is Indonesia's export destination country at number 31 and companies from Canada have also opened businesses in Indonesia and employ Indonesian employees. For that, Indonesia considers Canada as one of the potential countries whose market can be developed. In this regard, Indonesia views the importance of Canada as a country that can be used as a trade partner bilaterally, so a strategy is needed in exploring international trade cooperation in a bilateral scheme. This study aims to analyze the strategy of Indonesia's trade and investment cooperation in Indonesia-Canada. This study is expected to answer the strategies that can be applied by negotiators in the Indonesia-Canada CEPA trade cooperation negotiations To answer the research aims, we use the SWOT method, where the formulation of a potential strategy for developing trade cooperation in the Indonesia-Canada CEPA (ICA-CEPA) is carried out through three stages, namely the input stage, the matching stage and the decision-making stage. Based on the results of the Internal and External Matrix and SWOT, Indonesia is in an S-O (Strength and Opportunity) position, which means that Indonesia must use its strengths to take advantage of Indonesia's opportunities in the Canadian market, both in the trade in goods and investment sectors. The trade cooperation explored should also include discussions of economic cooperation and increased capacity building in negotiations, so that the human resource factor that is Indonesia's strength can compete with partner countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".