Mitigating donor interests in the case of COVID-19 vaccine: the implication of COVAX and DAC membership
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 vaccine donation process allegedly prioritised national interests over humanitarian needs. We thus examined how donors allocated vaccines by recipient country needs versus donor national interests and how such decisions varied across donation channels (bilateral vs COVAX with country earmarking) or exposure to foreign aid norms (membership status in the Development Assistance Committee-DAC). METHODS: We used the two-part regression model to examine how the probability of becoming a recipient country and the volume of vaccines received were associated with recipient countries' needs (disease burden and GDP per capita), donor countries' interests (bilateral trade volume and voting distance in the United Nations General Assembly) and recipient countries' population size. The analysis further interacted the determinants with channel and DAC status. RESULTS: Donors preferentially selected countries with higher disease burden, lower GDP per capita, closer trade relations, more different voting preferences, and smaller populations. Compared with bilateral arrangements, COVAX encouraged more needs-based considerations (lower GDP per capita), less interest-based calculus (more distant economic relations and voting preferences) and larger population size. Compared with the DAC counterparts, the non-DAC donors focused more on politically and economically aligned countries but also on less economically developed countries. As for the volume of vaccines donated, countries received more vaccines if they had tighter trade relations with donors, more different voting patterns than donors, and larger populations. COVAX was associated with raising the volumes of vaccines to politically distant countries, and non-DAC donors donated more to countries with stronger trade relations and political alignment. CONCLUSION: Donors consider both recipient needs and national interests when allocating COVID-19 vaccines. COVAX and DAC partially mitigated donors' focus on domestic interests. Future global health aid can similarly draw on multilateral and normative arrangements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".