At the threshold: A case study of a partnership between a student organization and an educational development center
Bibliographic record
Abstract
This article offers a case study about the collaboration between a student-led organization and an academic development unit dedicated to improving teaching and learning at [Institution 2]. We describe the genesis of our collaboration, how we nurtured and developed it over time into a substantive program, and what we learned in the process. While most existing case studies focus on partnerships between students and faculty, we turn the lens inward and investigate the challenges involved in enacting an “ethic of reciprocity” (Cook-Sather and Felten, 2017) in a partnership between an academic development center and a student organization. Using the analytical framework of threshold concepts, we explore the rocky navigating of issues of trust, vulnerability, role confusion, the notion of expertise, and pre-existing power inequalities to move towards a more collaborative and equitable 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".