Students-as-Partners versus Students-as-Employees: Division of Labour between Students, Faculty, and Staff in the McMaster Student Partners Program
Bibliographic record
Abstract
Many post-secondary institutions have implemented students-as-partners frameworks to redefine traditional educational practices and value students as co-creators of knowledge. The aim of this study was to investigate the degree to which students are working as partners and co-creators of knowledge with faculty and staff, versus replicating traditional hierarches. Herein, we undertook a multi-methods study consisting of a secondary analysis and a survey of one cohort of the student-as-partners program at McMaster University, as well as qualitative interviews. We found that some languages practices replicated traditional hierarchies, which was reflected in the degree to which partners contributed intellectually to the work undertaken. However, we also found meaningful shifts in practices occurred over the course of working collaboratively to foster more equitable partnerships. Herein, faculty and staff bore the responsibility of sharing power with student partners, but the blurring of professional and personal boundaries complicated the ethics of partnership.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".