Developing a guide to support the knowledge translation component of the grant application process
Bibliographic record
Abstract
Research funding agencies often expect that grant applications include a knowledge translation (KT) plan but many applicants and reviewers are without any clear idea of how to develop or assess such a plan. This paper arises from the development of a guide to support the KT component of the research grant application process. The authors reflect on some of the controversial issues related to KT definitions and scope that they struggled with as they developed the guide. Recommendations regarding implementation include training for reviewers and applicants as well as building broad infrastructure supports. The paper will be of interest to both practitioners and funders of research.
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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 | 0.121 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.039 |
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".