Towards complete development finance data: Quantifying China's international education co‐operation and presence in the Global South
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Motivation China does not participate in the development co‐operation reporting mechanism of the Organisation for Economic Co‐operation and Development's (OECD) development co‐operation reporting mechanism, nor does it voluntarily publish overseas development finance data. Despite recent quantitative research on China's foreign aid to other sectors, such as health, no precedent exists for quantifying China's international education co‐operation (IEC). Purpose This article will use AidData's Chinese Official Finance Dataset (AidData 2.0) to estimate the IEC using the OECD's internationally standardized definitions of development finance and frameworks for classifying IEC projects. Approach and methods We thoroughly examined all types of IEC projects, including official finance projects other than those that meet the definition of official development assistance (ODA). In our comparative analysis of educational aid between China and traditional donors, we focused on ODA‐like projects and examined the number of projects and funding amounts to determine China's IEC priorities. Findings The result shows that, between 2000 and 2017, China's IEC commitments totalled 1,524 education‐related international projects, representing 12% of the total international finance project portfolio, most of which are in Africa. Compared to the OECD framework, China prioritized higher education (n = 784, 51%) and education facilities and training (n = 244, 16%). An estimate of cumulative funding between 2000 to 2017 showed that China was the 10th largest donor of education aid to African countries, behind France, the World Bank, Germany, the United States, the EU, the United Kingdom, Canada, Japan, and the Netherlands. Policy implications The findings of this study help our understanding of China's IEC finance. With China's involvement in education development aid growing in recent years and donors looking for solutions to developing countries' debt crises, this will allow for more effective collaboration, co‐ordination, and resource mobilization for both donor and recipient countries.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it