Bibliometric Analysis and Funding Success to Evaluate an Organization’s Research Grant Decisions
Bibliographic record
Abstract
Title: Bibliometric Analysis and Funding Success to Evaluate an Organization’s Research Grant Decisions Objectives The Manitoba Medical Services Foundation (MMSF), a non-profit medical foundation that has provided nearly $20 million to support and fund research since 1974, sought to evaluate the subsequent output of both its successful and unsuccessful operating grant applicants. The foundation, which focuses on supporting new researchers, worked with the Library to determine whether its grant review process was successful in selecting the best candidates from 2008 to the 2012 competitions. Methods Using information up to 2014 for the five years of grants, which totaled $1,912,300 in funding, an analysis was first completed for all successful and unsuccessful grant applications. The analysis focused on two areas: publication history and funding history. Scopus – one of the largest databases in the world and a resource committed to eliminating author identification issues – was employed to determine the number of published articles and the h-index for each researcher. The funding databases of the three largest federal granting agencies in the country were searched to determine whether a researcher had subsequently obtained other grants. The bibliometric and funding data were statistically analyzed to assess the impact of a researcher’s initial grant result on their future publication output and funding success, as well as the local multiplier effect for the granting organization. Results Statistical analyses clearly demonstrated that those researchers who received funding from the MMSF went on to have greater academic productivity than unsuccessful candidates. Specifically, successful candidates had a greater number of publications, a higher h-index, larger amount of funding from the major Canadian research granting organizations, and greater odds of receiving funds as either co-investigators or lead principal investigators. Analyses also showed that successful applicants were ultimately very successful in bringing future external funding back to the province, with a local multiplier effect of 10:1 (i.e., for every $1 spent on Manitoba-based researchers, $10 returns to the community). Conclusions This research demonstrated that the current process used by MMSF is successful at selecting individuals who subsequently go on to become high-performing researchers. These researchers are ultimately more productive and obtain more funding than those individuals that are not selected. Furthermore, this project demonstrates a new way for Libraries to use metrics to assist organizations or institutions as they are called upon to demonstrate their value and impact on the community.
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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 | 0.052 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.133 | 0.157 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".