Foreign Direct Investment of Japan to Indonesia Post-Vaccine Diplomacy: Challenges and Opportunities
Bibliographic record
Abstract
This study aims to describe Japan's geopolitical opportunities and challenges in Indonesia after Indonesia-China vaccine diplomacy. In mid-2021, Japan donated 2.16 million doses of AstraZeneca to Indonesia which were sent on July 1 and 15 within the framework of bilateral diplomacy. The arrival of this vaccine is the right timing for Japan, where Indonesia is experiencing peak cases in two years (June-August 2021), where daily positive cases are above 40,000 cases and daily deaths are above 100 people. After delivery, the positivity rate decreased slowly, thus strengthening Japan's image as a trusted country in Indonesia. On August 25, this success resulted in the smooth lobbying of four major infrastructure development projects, namely the Patimban port, the construction of the MRT Jakarta Phase 2, the opening of the Makassar-Parepare railway line, and the Bekasi BPLJSKB Proving Ground. In addition, Indonesia offered investment and cooperation in opening five ports in other strategic locations, which Japan welcomed by relocating its factories from China to Indonesia, with a total of USD 2.6 billion by the end of 2021. Japan has full support from the Indonesian government to invest in strategic sectors under the framework of the Indonesia-Japan Economic Partnership Agreement (IJEPA) and the Regional Comprehensive Economic Partnership (RCEP). This is an opportunity for Japan to take back the position of the top five investors after falling to number seven in the first quarter of 2021, as well as to restore the economies of both countries in the midst of the pandemic. However, Japan must face challenges with South Korea and China who are scrambling to invest in Indonesia with a greater investment value, so that in the future, Japan must prepare strategies and increase other cooperation to maintain strategic partnerships and preserve hegemony in Indonesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".