Why is the proposal alone not sufficient for grant success? Building research fundability through collaborative research networking
Bibliographic record
Abstract
With current constraints of public funding of research the question of principal investigator (PI) success has gained prominence. Yet understanding grant success remains problematic. While often associated with peer acceptance of the proposal text, research fundability in large part is informed by PI participation in the social systems of research funding, knowledge making, and academic work. This article reports on part of an in-depth longitudinal exploration of how three expert science PIs in Canada navigate and negotiate these contexts to achieve sustained grant funding success. It focuses on how one of these scientists develops and draws on collaborative research networks in creating fundable front-line knowledge. The findings contribute towards an understanding of the role experiential learning and individual agency play in PI success. Practical implications for early-career researchers are outlined. Further questions about the interconnectedness of PI navigating and negotiating research funding and knowledge and academic work are raised.
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 | 0.238 | 0.452 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.033 | 0.021 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".