Online Education as a Solution to Managing and Sustaining Foreign Aid: Comparison Between the European Cases and Others
Bibliographic record
Abstract
Inbound education official development assistance (ODA) has been known to be spread across regions regardless of geographic proximity. This not only negatively impacts effectiveness to manage aid, but also to sustain aid long-term. This study examines aid disbursement pattern of the United Kingdom, Germany, Australia, and South Korea, which are all members of OECD’s Development Assistance Committee that allocates inbound education ODA. With the empirical results confirming Korea’s lack of concentration in education ODA, this study recommends establishing satellite campuses as a more viable, operative solution than the previously suggested solution of establishing a specialized agency focusing on scholarship programs. As validated by Nagoya University’s Asian Satellite Campuses Institute (ASCI), transferring much of work to the online platform reduces time and financial costs. Furthermore, satellite campuses are expected to facilitate various means of partnerships among aid donor countries that are implementing similar programs. International collaborative efforts could help improve the quality of inbound education and play an important role in attracting bright prospective students. Thus, donor countries could utilize online education platform to overcome severe geographic obstacles in distance education and increase effectiveness of its inbound education ODA.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".