Rural Futures - Mobilizing Knowledge and Sustaining Partnerships at the University of Guelph
Bibliographic record
Abstract
The future of rural places, people, and environments is critical to the province of Ontario. Launched in 2022, supported by the Ontario Agri-Food Innovation Alliance, the Rural Futures initiative explores ways to amplify the knowledge mobilization of rural research conducted at the University of Guelph and rural-based organizations across Ontario. This poster shares insights collected from rural partners, an inventory of student and faculty-produced rural research generated at the University of Guelph, and plans for future knowledge mobilization activities. Dialogues with rural partners illuminated valuable insights into the barriers preventing rural knowledge from reaching its intended audiences. While their knowledge-related needs differ significantly, every respondent noted the need for deeper collaboration between rural actors and a resource to help identify and facilitate opportunities for rural Ontarians. The project team has also collected and analyzed thousands of rural-related research items generated at the University of Guelph since 2010, identifying 5,112 articles. As a centre for rural knowledge, the initiative intends to build on these insights from the University of Guelph by supporting new knowledge mobilization activities. As part of this process, the research team is currently constructing an online pathfinder site that will act as a repository of rural research and a tool to help connect rural partners across Ontario to knowledge-based resources. These initial projects will continue to build on each other, strengthening Ontario’s rural communities and the agri-food sector in the process.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".