Partners, not peers: Defining boundaries and expectations in student partnerships
Bibliographic record
Abstract
In this reflection, one current student (Holly Pelnar), two former students (Grace Reyes and Kiran Sehgal), and one faculty member (Leslie Cameron) from Carthage College, a small liberal arts college in the United States, reflect on their experiences in student-faculty partnerships in the scholarship of teaching and learning (SoTL). From the faculty perspective, one of the most compelling aspects of conducting research in SoTL with undergraduate students as partners (SaP) is that the focus of the research is the students and their learning. Crucially, students also offer a unique perspective as they have relevant lived experience. From the student perspective, working as a student partner is motivating because we are working on topics that are relevant to our own learning and we have an opportunity to work closely with faculty in a unique research environment. In this essay we have collaboratively reflected on our experiences working together and identified challenging aspects of
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".