Learning the Development of Community-Engaged Scholars Through Course-Based Learning: A Student Perspective
Bibliographic record
Abstract
This manuscript chronicles the development of three graduate students as community engaged scholars, from the perspective of one of the students. With the support of the course instructor, a student (Thomas) and the instructor (Leah) discuss students’ development during their enrollment in a graduate course in community-engaged scholarship (CES) at the University of Guelph, a large comprehensive university in southwestern Ontario. Drawing from students’ reflection papers and progress reports, this article highlights students’ thoughts on communities’ perceptions of scholars; differences and similarities between community-engaged scholarship and more traditional forms of social science research; and challenges and opportunities of collaboration. Data highlighting students’ experiences with power relations, understandings of the need for adaptability within their respective partnerships, and acknowledgement of differences between community and academic roles in community-engaged research projects are also presented. Finally, the effects of large groups and imbalanced stakes on projects, and the influence of class-oriented timelines are discussed. The manuscript is written by, and from the perspective of Thomas Armitage, one of the students in the graduate course, in collaboration Leah Levac, the course instructor.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".