Creating a Teaching Community with Graduate Teaching Assistants: A Scholarly Personal Narrative
Bibliographic record
Abstract
Our purpose in writing this scholarly personal narrative is to share our perspectives and experiences as graduate student researcher and supervisor/principal investigator implementing and studying teaching and learning reform in the laboratory component of an undergraduate exercise physiology course. We reflect on our grappling with experiential learning, the need for reflection for it to happen, and what it means in the learning context. We also reflect on developing a community of practice with graduate teaching assistants, influencing teaching and learning culture through that community, and exploring the role of whole-heartedness and care in the process. We hope to support readers who feel compelled to reform or improve teaching and learning in their unique context.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.034 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".