Rethinking resource allocation in science
Bibliographic record
Abstract
Many funding agencies rely on grant proposal peer review to allocate scientific funding, i.e., researchers compete for funding by submitting proposals that are reviewed and ranked by committees of their peers.Only a fraction of applicants are awarded the requested funds.This system has a long and venerable tradition, but it is increasingly struggling to handle the larger number of applications, suffers from high levels of administrative overhead, may be unreliable in separating successful from unsuccessful projects, and may suffer from bias against innovative ideas, young researchers, and female scientists.We have proposed redesigning funding systems according to a few simple principles, namely, focusing on funding people instead of projects and involving as many scientists in funding decisions as possible.This underpins a proposal for a novel funding system in which every scientist periodically receives an equal, unconditional amount of funding but must anonymously donate a given fraction of everything he or she receives to other scientists of his or her choice.Over time, this simple process will lead to a funding distribution that reflects the entire scientific community, fosters young scientists, and reduces overhead.However, in spite of its simplicity, we must address certain challenges in its implementation such as deciding who participates in the funding system, how to control for conflicts of interest and bias, and how to manage its application.Funding agencies will play a pivotal role in the development and management of this system.
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 | 0.110 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".