Mentoring.ca: types of mentoring programs featured on Canadian postsecondary education websites
Bibliographic record
Abstract
Purpose No research to date has explored mentoring programs on Canadian postsecondary institution websites or the kinds of mentoring programs, if any, that are present online. This study examined 96 unique Canadian postsecondary institutional websites and the online presence of 420 unique postsecondary mentoring programs. Design/methodology/approach The researchers employed content analysis and emergent coding strategies to evaluate mentoring program information published on Canadian postsecondary websites. Findings The findings suggest that most mentoring programs with an online presence were peer (student-to-student or faculty-to-faculty) programs, followed by community member-to-student programs. Further, few programs (16) were student-to-faculty oriented, indicating that students could struggle to seek faculty mentorship if they desired it. However, of the 420 programs with an online presence, dozens of programs lacked enough information for the researchers to determine the stakeholders or purpose of the programs. Originality/value As the first study of its kind to evaluate mentoring program communication on Canadian postsecondary websites, this work informs mentoring program administrators on how to better communicate what their programs offer. Certain Canadian postsecondary institutions had an online presence for many more programs than did other institutions; for example, the University of Waterloo shared information on their website about 21 unique mentoring programs on its campus, whereas MacEwan University shared information about just two unique programs. This chasm represents a great deal of future research into the practice of how professionals communicate mentoring programs on postsecondary websites.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".