Bibliographic record
Abstract
Japan initiated the TICAD process in 1993. In 2016, it was held for the 6th time in Kenya, where Japan promised to invest USD 30 billion in the African continent by 2019. Examining the relationship between ODA and FDI from Japan for the case of Asia, it was clear that Japanese ODA results in a “vanguard effect” on FDI; yen loan, one of the types of ODA, helped the recipient Asian countries to attract three times larger FDI than the total amount of yen loans. In order to attract increased FDI from Japan, African countries need to win yen loans from Japan if wishing to get more Japanese FDI. Although Japan’s tied aid projects can facilitate Japanese companies’ expansion to the recipient countries, it is crucial for them to take into account the OECD-DAC rules. Specifically, upper middle income countries are not eligible for tied loans. Also it is beneficial to keep in mind Japan’s priorities for yen loan such as Global Environmental Problems and Climate Change, Health/Medical Care and Services, Disaster Prevention and Reduction and Human Resource Development. In addition, Japan’s new national effort to export high quality infrastructure (for example, by the Japan Overseas Infrastructure Investment Corporation for Transport and Urban Development (JOIN)) can benefit African countries that urgently need to upgrade urban infrastructure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".