What Types of Canadian Postsecondary Mentoring Programs Are Online
Bibliographic record
Abstract
Postsecondary mentoring has been found to be an effective practice for improving student retention and graduation (Crisp et al., 2017), with common postsecondary mentoring programs employing a peer, student-to-faculty, and student-to-community member orientations (Black & Taylor, 2018). However, no research has explored mentoring programs on Canadian postsecondary websites, nor has research articulated which kinds of mentoring programs have a presence on these websites. Subsequently, this study examines 96 unique Canadian postsecondary institutional websites and the online presence of 443 unique postsecondary mentoring programs. Results suggest most mentoring programs with an online presence are peer (student-to-student or faculty-to-faculty) programs, followed by student-to-community member programs. Additionally, very few programs (16) are student-to-faculty oriented, indicating that students may struggle to seek faculty mentorship if they desire it. However, of the 443 programs with an online presence, dozens of programs lacked enough information for the researchers to determine the stakeholders or purpose of the program: This may be problematic for those seeking a certain type of mentoring program on Canadian postsecondary websites. Moreover, certain Canadian postsecondary institutions facilitate an online presence for many more programs than other institutions, as the University of Waterloo shared online information about 21 unique mentoring programs on campus, whereas MacEwan shared information about 2 unique programs. Implications for mentoring-specific research, practice, and student development theories are addressed.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".