Triangular cooperation and the global governance of development assistance: Canada and Brazil as “co-donors”
Bibliographic record
Abstract
The global governance of development assistance is currently under a process of important transformation in many ways, with the rise of “emerging” donors – such as Brazil, China and India – prompting serious debates over concepts adopted by traditional donors and fomenting novel interactions. One strategy that brings together “emerging” and traditional donors is “triangular cooperation” (a.k.a “trilateral cooperation”); here, two “co-donors” join forces to promote development assistance initiatives in a third country. This article argues that countries can be motivated to engage in “triangular cooperation” for two main reasons: first, its likelihood of improved effectiveness for a lower cost; second: the opportunity it provides for the “co- donors” to strengthen their relationship. This article focuses on this second point, and explores it by analysing the potential for triangular cooperation between Canada and Brazil. The attention is on how this mechanism can help this traditional donor improve its ties not only with Brazil but with Latin American countries in general. Evidence suggests that while there are already levers in place to make this become a reality, there also important hurdles – such as the inherent complexity involved with triangular cooperation and the certain aspect involving the political interaction between Canada and Brazil.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".