What peer mentoring taught us about undergraduate and postgraduate partnerships
Bibliographic record
Abstract
In August 2017, two students and I (Jennifer) commandeered an empty conference room to draft the outline for what would become Grand Canyon University's (GCU) peer mentoring initiative. We envisioned peer mentoring as a curricular endeavor, where peer mentors (PMs) would attend first-year writing classes alongside enrolled students to model student behavior, facilitate discussions, and offer feedback. Because GCU already had instructional assistants (IAs) who are similar to graduate teaching assistants (GTAs) at other universities, clear distinctions were drawn between IA and PM job responsibilities to assuage administrative concerns about potential Family Education Rights and Privacy Act (FERPA) violations. Grading and classroom management, for example, were IA responsibilities; PMs were explicitly restricted from these activities. With PM duties codified, we secured administrative approval. That same semester, PMs entered the classroom.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".