Research-targeting, spillovers, and the direction of science: Evidence from HIV research-funding
Bibliographic record
Abstract
HIV/AIDS has been a major focus for research funders. The US National Institutes of Health (NIH) alone has spent over $70bn on HIV/AIDS. Such investments ushered in antiviral drugs, helping to reverse a rapidly growing HIV/AIDS pandemic. However, the idea that research can deliver unexpected benefits beyond its targeted field, in fact, predates HIV/AIDS to at least Vannevar Bush’s influential 1945 report. Cross-disease spillovers – research investments that yield benefits beyond the target disease – remains unexplored, even though it could inform both priority-setting and calculations of returns on research investments. To this end, we took a sample of NIH’s HIV grants and examined their publications. We analyzed 118,493 publications and found that 62% of these were spillovers. We used Medical Subject Headings (MeSH) terms assigned to publications to explore the content of these spillovers, as well as to corroborate non-spillovers. We located spillovers on a network of MeSH co-occurrence, drawn from the broader universe of biomedical publications, for comparison. We found that HIV spillovers were unevenly distributed across disease-space, and often in close proximity to HIV (60% local; 40% remote). We further reviewed 1,000 grant–publication pairs from a local sample and 1,000 pairs from a remote sample. For local spillovers, a quarter seemed to be unexpected, on the basis of their grant description; for remote spillovers, that proportion increased to one third. We also found that the NIH funding institutes whose remits were most closely related to HIV/AIDS were less likely to produce spillovers than others. We discuss implications for theory and policy.
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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 | 0.044 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".