Testing the claim of greater project effectiveness: a systematic review of comparative evaluations of locally led development
Bibliographic record
Abstract
In the sphere of foreign assistance, there is a growing push for locally led development (LLD). One common argument is that LLD approaches are more effective than traditional approaches for producing project outcomes. However, the empirical evidence for the claim of greater project effectiveness remains unexamined. This systematic review asks ‘what is the empirical evidence for the relative effectiveness of LLD approaches compared to traditional, or less locally led, approaches?’ We include studies that measure the comparative effectiveness of an LLD approach to a traditional or less LLD approach. We exclude studies that only compare an LLD approach to the status quo or doing nothing. Our index and website search (May 2024) plus snowball search yielded 1,749 hits. Ten studies passed all screening criteria. Study findings are mixed. Our main findings are: there is limited evidence on the relative effectiveness of LLD approaches compared to other development interventions; there is no evidence on relative cost effectiveness; and findings are mixed on the relative effectiveness of the same intervention depending on context and outcomes measured. We conclude that policy advocates should refrain from setting expectations about relative cost effectiveness and focus on other reasons for locally led development.Registration available OSF https://doi.org/10.17605/OSF.IO/E9FH3
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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 | 0.212 | 0.471 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".