Knowledge Mobilization Practices of Educational Researchers Across Canada
Bibliographic record
Abstract
Researchers are under increasing pressure to disseminate research more widely with non-academic audiences (efforts we call knowledge mobilization, KMb) and to articulate the value of their research beyond academia to broader society. This study surveyed SSHRC-funded education researchers to explore how universities are supporting researchers with these new demands. Overall, the study found that there are few supports available to researchers to assist them in KMb efforts. Even where supports do exist, they are not heavily accessed by researchers. Researchers spend less than 10% of their time on non-academic outreach. Researchers who do the highest levels of academic publishing also report the highest levels of non-academic dissemination. These findings suggest many opportunities to make improvements at individual and institutional levels. We recommend (a) leveraging intermediaries to improve KMb, (b) creating institutionally embedded KMb capacity, and (c) having funders take a leadership role in training and capacity-building.
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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".