Discovering the Configurations of the factors affecting the effectiveness of ODA: Application of QCA
Bibliographic record
Abstract
This paper aims to discover the characteristics of the ODA donor countries which conduct ODA projects effectively and to induce the less effective ODA donor countries to benchmark them so that all ODA donor countries can implement ODA projects effectively and also help all the recipient countries eventually to end their poverties. As time goes on, the size of ODA increases across the glove and however, there has little studies on how to effectively implement ODA projects, in particular. What should be noted in the field of ODA studies is that each ODA donor country has its own political, social and policy-related system, resulting in that only one model for benchmarking the effective ODA donor countries is not easy to be constructed. In other words, there should be more than one model to be benchmarked on the part of less effective countries. Against this background, this study attempts to analyse 23 ODA donor countries to find the configurations of the factors affecting the effectiveness of ODA projects. The analysis shows that two combinations of the factors associated with the effectiveness of the ODA projects were found. The first type combination, in which Canada is included, is that the capacity for innovation of government is high, the willingness to delegate power to lower tier is high, the size of the assistance per recipient country is relatively big, the size of the assistance per agency, which is involved in ODA projects, is small, the size of the assistance per ODA project is small. The second type combinations, in which Denmark included, is that the capacity for innovation is high, the willingness to delegate power is high, the size of the assistance per ODA agency is high, the size of the assistance per recipient country is small and the size of the assistance per ODA project is small. It is suggested that less effective countries regarding ODA project implementation should benchmark one of the two types so that they could be effective in terms of ODA projects implementation.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".