COVID-19: Learning from Past Funding Initiatives and their Dismissal in Southeast Asia
Bibliographic record
Abstract
This article examines philanthropic funding of past efforts to control emerging infectious diseases in Southeast Asia and China. The recount, based on personal insights as a foundation professional and a review of both published and unpublished material, shows that American foundations and other like-minded donors identified the risks associated with zoonotic infections early on – including from the same coronavirus family that is causing the current COVID-19 pandemic – and were later followed by bilateral and multilateral donors investing greater resources. At the cusp of the 2000s, foundations played a leadership and catalyst role in advancing a transdisciplinary agenda to better understand and respond to new emerging threats and in building the necessary individual and institutional capacities for regional and local disease surveillance. For more than a decade, this concentration of resources and approaches was recognised as having contributed to better preparedness. Gradually, however, funding initiatives declined in value and intensity due to several internal and external factors. This article argues that COVID-19 arrives in the midst of an unfinished donor agenda and that it is important to reflect on why philanthropic foundations, and the development aid community more generally, found themselves unprepared for the pandemic in order to draw lessons for addressing today’s crisis – and future outbreaks of emerging infectious diseases.
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".