“I Saw a Change”: Enhancing Classroom Equity through Student-Faculty Pedagogical Partnership
Bibliographic record
Abstract
Persistent inequities in access to and experiences of learning in postsecondary education have been well documented. In line with efforts to redress these inequities and develop more just institutions, this study explores the potential for pedagogical partnerships in which students and faculty collaborate on teaching and learning initiatives to contribute to classroom equity. We investigate this issue by drawing on qualitative interviews with students who have participated in extracurricular pedagogical partnership programs in institutions in Canada and the United States, and who identify as members of marginalized groups (e.g., racialized students, 2SLGBTQ+ students, students from religious minorities, disabled students). While much existing research on equity and student-faculty partnership primarily focuses on the outcomes of partnership for participating students, we instead investigate students’ perceptions of the extent to which their partnership efforts contributed to wider impacts—such as developments in faculty thinking and teaching practice and student experiences in the classroom. We also consider challenges students noted connected to power imbalances and faculty resistance, which influence partnership’s capacity to contribute to equity and raise important considerations for those interested in partnership practice.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".