Undergraduate curricular peer mentoring programs : perspectives on innovation by faculty, staff, and students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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 it