Undergraduate curricular peer mentoring programs : perspectives on innovation by faculty, staff, and students
Bibliographic record
Abstract
Introduction: The History and Scope of Curricular Peer Mentoring Programs Tania S. Smith Chapter 1: Defining Features of Curricular Peer Mentoring Programs Tania S. Smith Chapter 2: Discipline-Focused Peer Mentoring: Peer Teaching in Biology at the University of British Columbia Carol Pollock Theory and Practice: Lave and Wenger on Communities of Practice Tania S. Smith Chapter 3: Peer Mentoring in a Team-Taught Interdisciplinary Course: Engaging the 21st Century Student Through Peer-Led Learning Tina Pugliese, Tamsin Bolton, Veronika Mogyorody, Jill Singleton-Jackson, Robert Nelson & Ralph H. Johnson Theory and Practice: Student Engagement Tania S. Smith Chapter 4: Peer Mentoring in Large-scale First-year Programs: Academic Peer Mentors in First-year Courses at the University of Texas at Austin Jennifer L. Smith Theory and Practice: Tinto and Wenger on Learning Communities Tania S. Smith Chapter 5: Peer Mentoring in a Technical Institution: Undergraduate Mentoring in Software Engineering Sanjay Goel Theory and Practice: Vygotsky's and Bloom's Theories Tania S. Smith Chapter 6: Hosting Peer Mentors in a Senior Interdisciplinary Course: Notes from a Pre-History of Peer Mentoring at the University of Calgary Marcia Jenneth Epstein Theory and Practice: Bruffee on Collaborative Learning Tania S. Smith Chapter 7: Supporting Peer Mentors: Recruiting, Educating and Rewarding Peer Mentors Kate Zier-Vogel and Andrew Barry Theory and Practice: Peer Mentor Education Through Service-Learning Tania S. Smith Chapter 8: Case Studies of Conflict and Collaboration: Supporting Teaching Assistants Who Work with Peer Mentors Bryanne Young Theory and Practice: Teaching Teams with Graduate and Undergraduate Assistants Tania S. Smith Conclusion: Program Development and Sustainability Tania S. Smith
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".