A Campus-Wide Community-Engaged Learning Study: Insights and Future Directions
Bibliographic record
Abstract
The authors undertook a campus-wide scan of community-engaged learning (CEL) initiatives at a large University. With collaboration from staff and leadership of the campus Centre For Community-Engaged Learning, the researchers designed an open-ended qualitative interview and questionnaire for senior administrators and faculty leaders across all local undergraduate faculties. Guiding questions for this project included: How do the various faculties and schools within the university define their relationship with community? What activities are considered CEL? How do students engage in these activities? What are the benefits of engaging with community? From these came specific interview questions that were administered to senior administration from each faculty, and further interviews were sought with identified faculty leaders. Findings are listed by faculty, with examples and definitions, and a concluding section offers insights and discussion around strategies to strengthen and enhance CEL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.872 | 0.722 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.866 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.880 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".