Governing Foreign Aid: Explaining Donor Responsiveness to Global Development Goals
Bibliographic record
Abstract
In September 2015, the United Nations adopted the 17 Sustainable Development Goals (SDGs). Foreign aid is crucial to achieving the SDGs in developing countries, as it can fill key funding gaps that the private sector or NGOs cannot. However, there exists a fair amount of variation in the impact of the SDGs on the aid policies and practices of major OECD donors. Drawing on case evidence, including elite interviews and qualitative document analysis, from three bilateral aid donors (Sweden, Canada and the United Kingdom), this dissertation seeks to explain this variation and identify the causal mechanisms through which global goals influence aid policy and practice. I argue that the SDGs exert influence through a mechanism of show and tell in donor normative frameworks. Show and tell involves rhetorical performance that obligates performers to communicate their policies, practices and identities using a common script. The sensitivity of donors to this mechanism of show and tell is shaped by the perceived instrumentality of the SDGs in furthering donors’ specific reputational aspirations. When donors perceive that the SDGs will further their reputational aspirations they will show and tell a story about how their aid programs and even more fundamentally their donor identity are linked to the SDGs. This process of show and tell can lead to goal responsiveness because donors then link SDG implementation to the maintenance or achievement of particular donor identities. The variation in donors’ political commitment to the goals, then, is attributable to their differing sensitivity to the performative mechanism of show and tell which is in turn rooted in differing concerns for their reputation, and their perception of the SDGs utility in furthering their reputations. Donor governments who perceive that the SDGs are useful in achieving their desired reputation are more likely to be highly responsive to the goals.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".