Compassion, flexibility, and partnership in the midst of a global pandemic
Bibliographic record
Abstract
Our Students as Partners Program (SaPP) project took place throughout a global health pandemic that shook post-secondary institutions to their core.The COVID-19 pandemic disproportionately affected post-secondary students (Cao et al., 2020;Liang et al., 2020;Patterson et al., 2021) and created pedagogical challenges that most instructors had never imagined (Ramirez, 2020;Weinhandl et al., 2021).Despite many challenges, our student-faculty partnership resulted in a compassionate, flexible, and student-centered learning environment.This project helped both student and faculty partners endure a challenging time and provided a fun, engaging, and pedagogically sound learning experience where all students felt welcomed and connected. BACKGROUND AND CONTEXTThis SaPP project took place at Carleton University where the student and faculty partners were members of the Department of Neuroscience.At the time of this project, Dr. Zachary Patterson was a new instructor for the Department of Neuroscience, Dr. Kim Hellemans was a senior instructor in the Department of Neuroscience and the departmental chair, Izzy Munevar-Pelton had just completed her second year of the Neuroscience and Biology Combined Honours program, and Anika Olsen-Neill had just finished her second year of the Neuroscience and Mental Health program
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".