By any other name? The impacts of differing assumptions, expectations, and misconceptions in bringing about resistance to student-staff partnership
Bibliographic record
Abstract
Most of the existing literature on student-staff partnership explores the experiences of people who are keen to be involved and who have already bought into the ethos of Students as Partners. We explore the challenges of conducting student-staff partnership in the context of resistance. Specifically, we focus on the interpretations of partnership by students and staff who were attempting to work in partnership for the first time in a medium-sized geography department in the UK The views of participants were captured during a six-month project in which four undergraduate students were employed to work with eight academics to redesign the second-year undergraduate curriculum of one programme. Notwithstanding an introductory briefing and ongoing support, some participants showed indications of resistance. Our findings suggest that different perspectives on partnership influenced participants’ experiences. We argue that assumptions, expectations, and misconceptions around the terminology used to describe Students-as-Partners practice may hinder the process itself, as some people may not buy in to the practice. However, despite the challenges of this project, the experience of being involved in the re-design of the modules has led to reduced resistance and emerging partnership practices throughout the department.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".