New Perspectives of Reciprocity in Community-Engaged Learning: A Case Study of a First-Year Post-Secondary Knowledge Exchange Project in an Over-researched Urban Community
Bibliographic record
Abstract
This paper describes key discoveries and lessons learned about the practice of reciprocity in community-engaged learning (CEL). We draw from an example of a multi-partner, multi-year, CEL project that addresses a community-identified priority to access jargon-free research findings about their community. Our project benefits community members in an over-researched, equity-deserving, inner-city neighborhood without requiring the direct presence of large numbers of university students in the community. In this collaboration, first-year undergraduate students in introductory academic writing courses at the [Canadian post-secondary institution] create publicly accessible infographic summaries of research articles arising from studies that have taken place in [an over-researched inner-city] neighborhood. First-year students, in their position as novice scholars, bring helpful perspectives to the task of knowledge translation. As apprentice researchers not yet immersed in disciplinary languages, they are cognizant that the specialized types of discourse used in research writing are often not accessible to readers outside the academy. Pairing students with community-engaged researchers leads to multi-directional benefits: students develop their knowledge translation skills in an authentic research writing situation; researchers benefit from publication of supervised, student-authored infographics of their scholarship; and over-researched communities gain access to relevant research findings. A community-embedded institutional unit is crucial to the project’s success, providing the resources, relationships, and boundary-spanning expertise required to ensure this project is successful from the perspective of the community and the university.
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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 | 0.035 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.048 | 0.033 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.007 | 0.009 |
| 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".