Exploring Mentorship in Higher Education: Introduction to the Fifth Volume of Papers on Postsecondary Learning and Teaching
Bibliographic record
Abstract
The fifth volume of Papers on Postsecondary Learning and Teaching (PPLT) is a collection of papers from the 2021 University of Calgary Conference focused on the theme of Mentorship in Higher Education.Since the 2020 conference was cancelled due to the COVID-19 pandemic, PPLT heartily welcomes back authors this year for its fifth volume.Although the pandemic has caused disruptions and transitions in postsecondary research, learning and teaching, it has revealed insights for both students and academics.The papers in the volume are organized to reflect the stages of the academic lifespan, from student to professor emeritus.Perspectives from multiple disciplines present the shared common theme that all forms of mentorship in higher education are beneficial to students, instructors, the institution, and graduates. Mentorship ModelsLorelli Nowell (2022), one of the conference keynote speakers, presents in her paper, "Beyond tradition: Innovative mentorship models for higher education", a comprehensive overview and description of the various models of mentorship, which includes peer, group, distance, and constellation models.Her introduction sets the stage for the rest of the papers in this volume as they explore various models from diverse perspectives and disciplines to further our understanding and practice of mentorship in higher education.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".