Bibliographic record
Abstract
The National Institutes of Health (NIH) plays a critical role in funding scientific endeavors in biomedicine that would be difficult to finance via private sources. One important mandate of the NIH is to fund innovative science that tries out new ideas, but many have questioned the NIH’s ability to fulfill this aim. We examine whether the NIH succeeds in funding work that tries out novel ideas. We find that novel science is more often NIH funded than is less innovative science but this positive result comes with several caveats. First, despite the implementation of initiatives to support edge science, the preference for funding novel science is mostly limited to work that builds on novel basic science ideas; projects that build on novel clinical ideas are not favored by the NIH over projects that build on well-established clinical knowledge. Second, NIH’s general preference for funding work that builds on basic science ideas, regardless of its novelty or application area, is a large contributor to the overall positive link between novelty and NIH funding. If funding rates for work that builds on basic science ideas and work that builds on clinical ideas had been equal, NIH’s funding rates for novel and traditional science would have been the same. Third, NIH’s propensity to fund projects that build on the most recent advances has declined over the last several decades. Thus, in this regard NIH funding has become more conservative despite initiatives to increase funding for innovative projects.
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 | 0.035 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".