Sailing through a storm: The importance of dialogue in student partnerships
Bibliographic record
Abstract
The following is our collective attempt—staff- and student-centric, both in terms of outcomes and reporting—to unpack the complexities of our collaborative endeavour in 2017. We juxtapose our respective experiences of navigating the “normative hierarchical university paradigm” (Mercer-Mapstone et al., 2017, p. 18) to present a more collaborative and balanced discussion of our partnership. We reflect on our “way of doing things” (Healey, Flint, & Harrington, 2014, p. 12) so that the partnership process is more visible, particularly in relation to the challenges and negative outcomes. An ethos of reciprocity (Matthews, 2017) influenced our thinking and practice, and we were acutely aware of the complexities involved in real-life exchanges between staff and students. We discussed power openly throughout our collaboration, and here we speak about its function as equal co-authors of our empirical story. We are frank about the challenges that we faced and do not shy away from discussing failures, as well as lessons learned. We hope that this will help others to critically analyse and reflect on their own practice and, in the process, fully explore the transformative power of student partnerships for individuals and their institutions.
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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 | 0.069 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.063 |
| Scholarly communication | 0.047 | 0.039 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".