Rural Futures: Bridging Research and Community Solutions for a Resilient Ontario
Bibliographic record
Abstract
The Rural Futures project has been serving as a vital conduit for knowledge exchange, facilitating connections between rural researchers and stakeholders. Insights gleaned from an evaluation process have informed our future endeavours as researchers emphasize the importance of disseminating findings to non-academic audiences, while stakeholders recognize the potential of current research to address local challenges. This poster presents our initial plan for bolstering knowledge mobilization, drawing on the insights gathered from the evaluation. There is a clear demand for diverse knowledge products tailored to various sectors and contexts, as highlighted by the research team. Also, student researchers at the University of Guelph advocate for further customization of the project website, which serves as a valuable resource for accessing rural reports and profiles. The project sustainability plan involves an approach that aims to improve partnerships and dialogue, strengthening connections between stakeholders and knowledge producers. By linking research to community solutions, Rural Futures will continue to foster symbiotic relationships, providing employment opportunities for researchers and enhancing community resilience in Ontario.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".