Ethics in Community-University Partnerships — with Kari Grain
Bibliographic record
Abstract
Kari Grain is a practitioner-scholar at the intersection of higher education, social justice, and community engagement. She earned her PhD in Education at UBC as a Vanier scholar, where her research focused on local community impacts of international service-learning in Uganda. For the past five years, she has worked as an educational consultant, focusing on experiential education, community-engaged research, and the scholarship of teaching and learning. Her research has been published in the Journal of Experiential Education, the Michigan Journal of Community Service Learning, and the Canadian Journal of Studies in Adult Education. She is currently a sessional instructor in UBC’s Faculty of Arts and Faculty of Education and has been collaborating in varying capacities with SFU scholars since 2017's Community2University Expo.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".