Cost of the NSERC Science Grant Peer Review System Exceeds the Cost of Giving Every Qualified Researcher a Baseline Grant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using Natural Science and Engineering Research Council Canada (NSERC) statistics, we show that the $40,000 (Canadian) cost of preparation for a grant application and rejection by peer review in 2007 exceeded that of giving every qualified investigator a direct baseline discovery grant of $30,000 (average grant). This means the Canadian Federal Government could institute direct grants for 100% of qualified applicants for the same money. We anticipate that the net result would be more and better research since more research would be conducted at the critical idea or discovery stage. Control of quality is assured through university hiring, promotion and tenure proceedings, journal reviews of submitted work, and the patent process, whose collective scrutiny far exceeds that of grant peer review. The greater efficiency in use of grant funds and increased innovation with baseline funding would provide a means of achieving the goals of the recent Canadian Value for Money and Accountability Review. We suggest that developing countries could leapfrog ahead by adopting from the start science grant systems that encourage innovation.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Simulation or modeling | medium |
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 | 0.088 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it