Explaining the Driver Factors Behind Indonesia’s Decision on the Indonesia-Canada Comprehensive Economic Partnership Agreement
Bibliographic record
Abstract
This article explains Indonesia’s decision to sign a Comprehensive Economic Partnership Agreement with Canada by assessing how economic and political motives interact. The study uses a qualitative descriptive design with a desk review of official trade and tariff data and policy documents from 2009 to 2025. The findings show strong complementarity between Indonesia’s export structure and Canada’s demand in manufacturing and the light industry. Before the agreement, most-favoured-nation tariffs on apparel of about 17.3 percent and on leather goods of about 16.1 percent created a price gap relative to suppliers that already enjoyed preferences. The agreement narrows this gap through staged tariff elimination and clearer operational rules that facilitate the use of preferences. Politically, the partnership broadens Indonesia’s cross-regional network, adds a direct link to a G7 economy, and supports Indonesia’s standing in ASEAN. The study concludes that Indonesia’s choice reflects mutually reinforcing economic and political drivers that are expected to reduce tariff discrimination and increase the predictability of market access.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".