Bibliographic record
Abstract
Abstract: This paper revisits the issue of aid effectiveness in Africa by examining the effect of aid on growth. Historically, Africa's development context appears to be an aid‐dependent one, and with the New Partnership for Africa's Development (NEPAD) calling for additional capital flows to improve growth levels on the continent, and the attainment of the UN's Millennium Development Goals partly conditioned on aid inflows, there is a new urgency to evaluate the effectiveness of aid. Using a sample comprising 40 member countries of the African Union, and estimating fixed‐effects growth models, we find a positive and statistically significant effect of aid on growth. Aid increases investment, which is a major transmission mechanism in the aid‐growth relationship. An extension of our analysis to examine sources of growth finance shows aid, workers' remittances, debt‐service resources and domestic savings are important sources of development finance. Thus, for now, aid matters for the continent's growth. However, given the apparent donor aid fatigue and the debt servicing implications of concessional loans, the paper supports the need to strategize to reduce future dependence on aid.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".