Science policies: How should science funding be allocated? An evolutionary biologists’ perspective
Bibliographic record
Abstract
In an ideal world, funding agencies could identify the best scientists and projects and provide them with the resources to undertake these projects. Most scientists would agree that in practice, how funding for scientific research is allocated is far from ideal and likely compromises research quality. We, nine evolutionary biologists from different countries and career stages, provide a comparative summary of our impressions on funding strategies for evolutionary biology across eleven different funding agencies. We also assess whether and how funding effectiveness might be improved. We focused this assessment on 14 elements within four broad categories: (a) topical shaping of science, (b) distribution of funds, (c) application and review procedures, and (d) incentives for mobility and diversity. These comparisons revealed striking among-country variation in those elements, including wide variation in funding rates, the effort and burden required for grant applications, and the extent of emphasis on societal relevance and individual mobility. We use these observations to provide constructive suggestions for the future and urge the need to further gather informed considerations from scientists on the effects of funding policies on science across countries and research fields.
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 | 0.084 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".