A taxonomy of the characteristics of student peer mentors in higher education: findings from a literature review
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
Peer mentoring in higher education is regarded as an effective intervention to ensure the success and retention of vulnerable students. Many universities and colleges have therefore implemented some form of mentoring program as part of their student support services. While considerable research supports the use of peer mentoring to improve academic performance and decrease student attrition, few studies link peer mentoring functions with the type of peer best suited to fulfill these functions. This literature review categorizes the abundant student peer mentor descriptors found in mentoring research. The result is a preliminary taxonomy that classifies ten peer mentor characteristics according to mentoring function served (career‐related or psychosocial). The proposed taxonomy and the discussion developed in this article help shed light on the dynamics of successful student peer mentoring relationships in higher education.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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