Building Bridges: A Practical Guide to Developing and Implementing a Subject-specific Peer-to-peer Academic Mentoring Program for First-year Higher Education Students
Bibliographic record
Abstract
The Telemachus Ancient History Mentor Program (informally known as Tele’s Angels) has been offering peer-led transition services to first-year students at Macquarie University since 2002. Tele’s Angels volunteer Mentors create a ‘learning community’ by providing their first-year colleagues with transition assistance, academic support and resources, and networking for and amongst students and staff. Individual mentoring is offered, as well as free peer-support services which focus on developing academic skills and building social networks. The program also focuses on student leadership – a key objective is that Mentors themselves are beneficiaries of all activities, embodying the program motto: “to give is to receive”. It is timely to report Tele’s Angels’ experiences to a wider audience and offer practical guidelines to those wishing to develop and implement subject-specific academic mentoring programs for first-year students in their own institutions.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.035 |
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".