Focus‐Pocus? Thinking Critically about Whether Aid Organizations Should Do Fewer Things in Fewer Countries
Bibliographic record
Abstract
Abstract The OECD Development Assistance Committee and G7 Finance Ministers have suggested that many bilateral and multilateral aid organizations are too dispersed, pursuing too many objectives in too many countries and too many sectors with too many partners. These organizations are accused of lacking critical mass, failing to follow their comparative advantage, failing to find and exploit a niche, and having high transactions costs and low effectiveness. Such aid organizations are being told to ‘focus’, ‘concentrate’, or be more ‘selective’ in order to become more effective. This article analyses the arguments in favour of greater focus by aid organizations and suggests that, while some of these arguments are valid, some are not and others need to be more nuanced. There are many possible dimensions along which an aid organization could focus and the link — if any — between focus and aid effectiveness is complex along each of those dimensions. The debate so far has also ignored the possibility that less focus may promote more effective aid. There is no clear, simple link between focus and aid effectiveness, but this finding should not be interpreted as carte blanche for spreading aid programmes indiscriminately. Dispersion, like focus, needs careful thought and justification.
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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 | 0.034 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.011 | 0.033 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".