Bibliographic record
Abstract
In response to critiques of current foreign aid distribution practices failing to achieve human development goals, scholars have argued for an alternative aid system. The concept of ‘smart aid’ seeks to improve the foreign aid system through fostering equitable and sustainable growth by means of selectively addressing locally determined development goals. Scholars, such as William Easterly, argue that the current top-down structures of foreign aid result in mismanaged funds that do not align with grassroots issues or agendas. Dambisa Moyo, a prominent international development economist, has also been a vocal critic of foreign aid; in claiming that aid regimes today have resulted in the formation of kleptocracies throughout various regions of Africa. The massive flows of international finance between the global North and South have resulted in a systemic lack of ‘good governance,’ through failing to address human development goals in favour of facilitating economic objectives. As such, smart aid focuses on the two central principles of feedback and accountability, to rectify the failures of current aid regimes and account for the self-identified development issues of aid recipients. By working with recipient communities, smart aid provides the foundation to fund innovative solutions to local problems relating to health, education, and human development. The primacy of both feedback and accountability, as part of the smart aid paradigm, intends to ensure that human development goals are reached by means of community mobilization and monitoring. By shifting the focus of development onto local communities, communal solutions can be implemented to address self-identified human development issues. The concept of smart aid therefore provides an opportunity to break from traditional aid practices, while better seeking to improve the basic standards of living in developing countries.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.080 |
| Scholarly communication | 0.018 | 0.038 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".