It’s hard to ignore the data when the data is in the room
Bibliographic record
Abstract
A growing body of research demonstrates the benefits of engaging students as partners to improve tertiary education. Yet, more research is needed to understand how students can support critical transformations outside of the classroom context. In this qualitative study, we explored how a networked improvement community (NIC) engaged students as partners toward critically transforming introductory tertiary mathematics courses in spring 2023. Using an open coding process, we analyzed field notes, interviews, and journals from NIC members to develop themes describing the NIC’s positioning of students. We compared these themes to Holen et al.’s (2021) framework on student-institutional partnerships. Findings reveal four positions students may adopt in critical transformation efforts: democratic participant, apprentice, consultant, and beneficiary. This study contributes to the field’s understanding of ways students can influence larger structural and cultural systems that impact student success, as well as challenges inherent in this work.
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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 | 0.148 | 0.441 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".