The Co-produced Pathway to Impact Describes Knowledge Mobilization Processes
Bibliographic record
Abstract
Knowledge mobilization supports research collaborations between university and community partners which can maximize the impacts of research beyond the academy; however, models of knowledge mobilization are complex and create challenges for monitoring research impacts. This inability to sufficiently evaluate is particularly problematic for large collaborative research networks involving multiple partners and research institutions. The Co-produced Pathway to Impact simplifies many of the complex models of knowledge mobilization. It is a logic model based framework for mapping the progress of research -> dissemination -> uptake -> implementation -> impact. This framework is illustrated using collaborative research projects from Promoting Relationships and Eliminating Violence Network (PREVNet), a pan-Canadian community-university network engaging in knowledge mobilization to promote healthy relationships among children and youth and prevent bullying. The Co-produced Pathway to Impact illustrates that research impact occurs when university researchers collaborate with non-academic partners who produce the products, policies, and services that have impacts on the lives of end beneficiaries. Research impact is therefore measured at the level of non-academic partners and identified by surveying research partners to create narrative case studies of research impact.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".