How are Educational Researchers Interacting with End-users to Increase Impact?
Bibliographic record
Abstract
There has been increased interest in how researchers might collaborate with end users to increase the impact of their work. In Canada, efforts to extend research impact beyond academia are called knowledge mobilization (KMb). This study surveyed SSHRC- funded educational researchers to assess their KMb efforts in relation to three areas: stakeholder engagement (target audience and frequency of interaction), dissemination mechanisms (intermediaries, networks, media, online tools), and research impact (research-related, service/practice, policy, societal). Findings: 70% of researchers reported regularly interacting with target audiences. Types of interactions included getting to know target audiences (71%), discussing research results (65%), and dedicating resources for capacity building (45%). Researchers reported impacts in relation to research (76%), service/practice (67%), and policy (35%), and societal impacts (35%). Researchers felt very well prepared to create plain language summaries of their work (54%), and collaborate with stakeholders (45%), but much less prepared to deal with media (32%), work with intermediaries (22%), or use technology to disseminate their work (16%). Implications for engaged scholarship are articulated in five areas: prioritization and co-production; packaging and push; facilitating pull; exchange; and improving climate for research use by building demand.
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 | 0.207 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.040 | 0.038 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".