Emerging Donors in International Development Assistance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Brazil (authored in English by Alcides Costa Vaz and Cristina Yumie Aoki Inoue, translated by A. Shvets, ed. by O. Perfilieva)India (authored in English by Subhash Agrawal, translated by M. Rakhmangulov, ed. by O. Perfilieva)China (authored in English by Gregory T. Chin and B. Michael Frolic translated by Y. Zaytsev, ed. by V. Nagornov)South Africa (authored in English by Wolfe Braude, Pearl Thandrayan, Elizabeth Sidiropouls, translated by A. Shadrikova, ed. by V. Nagornov)The article presents the translation of a series of papers on the role of emerging economies in international development assistance prepared under the project “Emerging Donors in International Development Assistance” supported by the International Development Research Centre, Canada in 2007-2008. The papers review the lessons revealed by the country studies of Brazil, China, India, and South Africa, who are referred to collectively as ‘emerging donors’. The studies emphasize the origins, structure, and operations of these countries’ development assistance programmes, especially the research for development and international collaborative dimensions, geographical and sectoral priories of their programmes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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