DOES GOVERNMENT FUNDING HAVE THE SAME IMPACT ON ACADEMIC PUBLICATIONS AND PATENTS? THE CASE OF NANOTECHNOLOGY IN CANADA
Bibliographic record
Abstract
University patenting has become an important research outcome in the past few decades. There has been an increase in the number of faculty patents and individual scientists listed as inventors on patent applications. The effective allocation of funding to universities is of great concern to policymakers. In this paper, we evaluate whether an increase in government funding for academic scientists enhances the performance of researchers in both scientific publications and academic patents or if this merely increases publications in the academic realm. We provide summary statistics from nanotechnology data in Quebec, compare it with other provinces in Canada, and build econometric models of various publication, patenting and grant databases. The analysis illustrates the strong relationship between funding and publication productivity as well as the citation impact of publications. In the light of research performance in patenting activities of academic researchers, this empirical study finds a strong influence on the number of patents. Moreover, increased funding appears to strengthen the citation impact of patents in Quebec, which affects the citation impact of patenting activities.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".