A Study of the Refugee Policy and In-Donor Refugee Costs in ODA: A Case Study of Italy and Canada, and Its Implication on South Korea
Bibliographic record
Abstract
Purpose: What is in-donor refugee costs in ODA, and through what standards and procedures is it used for refugees in donor countries? What are the theoretical and policy implications and issues of in-donor refugee costs in ODA? This article aims to establish the concept and types of “in-donor refugee costs in ODA,” and to find policy implications for South Korea. Originality: As the refugee crisis has recently emerged as a global phenomenon, the international community is making efforts to support economic, social, and institutional development beyond humanitarian aid to refugee-receiving countries. Nevertheless, the refugee system within the donor country is still an unfamiliar concept in Korean society and has not received much attention academically. This situation increases necessity for research about Korea’s in-donor refugee costs in ODA. Methodology: This article attempts a comparative analysis of cases of two countries that have utilized in-donor refugee costs in ODA, and tries to draw implications for Korea’s in-donor refugee costs in ODA policy. Using various governmental reports and data released by international organizations, the authors conduct a comparative case analysis of Canada’s and Italy’s policies of in-donor refugee costs. Result: The result shows that there are common characteristics in the purpose, priority, and budget proportion in Canada’s and Italy’s policies of in-donor refugee costs in ODA. However, it can be claimed that South Korea’s proportion of in-donor refugee costs in ODA out of its total bilateral ODA was quite low; it’s policy has been implemented in a relatively passive manner. Conclusion and Implication: South Korea needs to increase the share of in-donor refugee costs in ODA through budgetary increase, and needs to carry out its policy in a more active manner by providing sufficient budget and establishing favorable environment for offering a basis for social integration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".