Charting the Trajectory of a Flexible Community-University Collaboration in an Applied Learning Ecosystem
Bibliographic record
Abstract
Current fiscal cuts provide numerous challenges for community organizations in their mission to provide evidence-based services. Universities are focusing on career-related experiences, largely experiential learning opportunities, to support enhanced student outcomes. Community engagement is often touted as a goal for universities and community collaboration is increasingly viewed as favourable in research. Thus, a community-university partnership which focuses on evaluation would serve to meet the needs of both groups currently experiencing challenges in service delivery and training, respectively. This article presents a case study of a community-university partnership between Renascent and Ryerson University that has evolved over time to meet the needs of both partners. We discuss the applied learning ecosystem, which extends from the supervisory context to the history of the academic institutional partner. We also discuss the flexibility in collaboration, noting the change over time to meet the evolving needs of both the university and the community partner. We aspire to contribute to the literature documenting the range of community-engaged partnerships by providing experiences and reflections to support others in this area.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".