University Research Funding: More than Supporting the Best to Do the Best
Bibliographic record
Abstract
According to Howard Birnbaum, the “‘margarine method’ of spreading research funds equally thin among all possible recipients is a waste of resources.”Quite the contrary. Despite the insulting sound, “margarine funding” is the best way to encourage serendipity, creativity, and originality in research. All university professors are expected to be efficient teachers and researchers. The highly competitive system of faculty appointments assures that, with rare exceptions, all university professors have the ability and training for both of those roles. Although equal grants for all are indeed impractical, there are viable and fiscally responsible alternatives to the present all-or-nothing funding model.If we keep in mind the known rule of economics that the first dollars are the most cost-efficient, the funding model under which all active university researchers receive a small default grant, say, $3000 to $5000 per year (but could apply for higher amounts on a competitive basis, if they wish), makes much more sense, both economically and socially, than the “winner takes all” selectivity model. The award of such a minimal grant should be based only on evidence of ongoing productivity—for example, one or two peer-reviewed papers each year. No proposal writing should be required for these default grants, apart from perhaps a one-page summary that should not require a separate peer review if copies of the applicant’s peer-reviewed papers are attached. More details and an extensive bibliography can be found in reference 1 1. A. A. Berezin, Interdisciplinary Sci. Rev. 26(2), 97 (2001)..REFERENCESSection:ChooseTop of pageREFERENCES <<1. A. A. Berezin, Interdisciplinary Sci. Rev. 26(2), 97 (2001). Google ScholarCrossref© 2002 American Institute of Physics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.170 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 teacher head, 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".