Triadic partnerships: Evaluation of a group mentorship scheme
Bibliographic record
Abstract
We synthesised views and experiences of three teams (student mentees, alumni mentors, and staff) in our pilot mentorship scheme within a distance learning MSc, evaluated the scheme, and developed a conceptual model of “triadic partnerships.” Thematic analysis of our qualitative data revealed a strong consensus across all teams. The triadic partnerships were reported to help reduce the feeling of “distance” in distance learning. Through developing triadic partnerships, our mentorship scheme provided added value beyond that offered previously by staff alone: credible and relatable authenticity within supportive mentoring by alumni. Since the scheme’s launch, student engagement has increased, with high levels of reported satisfaction and positive feedback and greater confidence among all teams. Our research connects the framework developed by Healey et al. (2014, 2016) to the literature on mentoring, offering a conceptual model on triadic partnerships. We encourage readers to consider the different relationships within multidimensional student partnerships in their own contexts.
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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 | 0.009 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".