Passing the (proverbial) baton: Engaging students as partners in module design
Bibliographic record
Abstract
Student-staff partnership, understood as the situation when students and staff work together on a project, contributing equally but in potentially different ways, is an innovation that is gaining traction on university campuses worldwide. This case study details my first foray into the partnership arena. I invited undergraduate students from the Schools of Law and Geography, Geology, and the Environment at Keele University to partner with me in designing a new optional module–Contemporary Issues in Environmental Law. My aim here is to provide an honest warts-and-all account of the experience, written from my perspective. I will document the positive outcomes for both the students and staff participating, of which there were many, but also some of the challenges faced. Despite these challenges, there can be no doubt that student-staff partnership is a worthwhile endeavour, and I hope others can view this as an example that can be adapted to suit their specific contexts.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".