Visualizing Rural Research: Enhanced Knowledge Translation Pathways Between University of Guelph and Rural Ontario Communities
Bibliographic record
Abstract
Despite the wealth of rural research being conducted, a persistent gap exists between academic findings and their practical application in rural communities. This project showcases the University of Guelph's innovative approach to bridging this divide through enhanced knowledge translation and transfer mechanisms. Our initiative transforms complex research findings into accessible, visual formats including plain language summaries, research videos, and infographics, specifically designed for diverse rural audiences across Ontario. By focusing on the interconnected domains of rural people, environments, and places, we create multiple entry points for stakeholder engagement with evidence-based solutions. Preliminary results demonstrate how visual knowledge translation tools strengthen the bidirectional flow of information between researchers and rural end-users, leading to more effective adoption of research findings in community practice. This work represents a significant step forward in making academic research more accessible and actionable for rural communities, businesses, and organizations facing complex, multidimensional challenges. Our findings emphasize the importance of collaborative knowledge mobilization in fostering resilient rural futures and offer a replicable model for other institutions engaged in rural research dissemination.
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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".