Bibliographic record
Abstract
This article analyzes the impact of the COVID-19 pandemic on foreign aid. Using examples from Canadian foreign aid, it argues that, despite the terrible toll it is exacting, the crisis has accelerated some significant positive pre-existing trends, both by destabilizing the perception of aid as flowing essentially from the Global North to Global South and by reinforcing awareness of the importance of joint efforts for global public goods and humanitarian assistance, as well as debt relief. However, it has also reinforced potentially harmful self-interested justifications for aid, which could align assistance more with donors' priorities than the needs of the poor. An important trend reversal is the renewed emphasis on well-being. Two other crucial trends remain unclear-the COVID-19 pandemic's impact on multilateral approaches and on aid flows. How donors respond to the COVID-19 pandemic and its aftermath over the next few years will depend on their political will, and will profoundly shape the future of development co-operation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".