MySci Advisors: Establishing a Peer-Mentoring Program for First Year Science Student Support
Bibliographic record
Abstract
Would you like to help your students adjust to university life? Perhaps you are simply interested in allowing them to feel more integrated into a department right from the start of their first year? These were the types of issues that we were hoping to address when we founded the MySci Advisors Program, a peer-mentoring group for first year students in the Faculty of Science at the University of Windsor. This program is run entirely on a volunteer basis with no working budget, so if you were considering starting a mentoring program but have been concerned about the cost of doing so, this essay may be of particular interest to you. MySci Advisors is only in its third year currently, so this essay is meant to focus on the lessons we have learned in the early establishment of the program. I outline some of the practices we have adopted for the program, some of the changes we have had to make along the way and provide some early evidence of success. It is my hope that others may be motivated to also form such a program or use this information to assist in their own early endeavours.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".