How impact-focused funding influences researchers’ knowledge mobilization activities
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
Abstract This study explores the influence of impact-focused funding on the knowledge mobilization (KMb) activities of federally funded researchers in Canada, focusing on recipients of the Natural Sciences and Engineering Research Council of Canada Discovery Grants. The findings challenge assumptions that funding programs emphasizing societal impact reliably lead to increased engagement in KMb activities. By examining pre- and post-funding KMb engagement across disciplines, the study reveals that while a small subset of researchers increased their KMb efforts, a larger proportion disengaged after receiving funding. These results point to significant barriers, including insufficient institutional support, disciplinary norms, and competing academic priorities, which may hinder the alignment of funding agency goals with researcher practices. The study also sheds light on discipline-specific and role-based variations, such as lower KMb engagement in applied fields and among researchers with administrative responsibilities. This research contributes to literature by identifying complications and unintended consequences associated with impact-driven funding mechanisms. The findings have implications for policymakers, funding agencies, and universities working to enhance the societal impact of publicly funded research.
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.
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 | 0.061 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it