Mentoring and professional identity formation for teaching stream faculty
Bibliographic record
Abstract
Purpose Peer-to-peer (P2P) mentorship has been identified as an important component of professional identity formation in higher education (HE). This may be especially true for education-focused or teaching stream (TS) faculty to thrive in times of changing organizational structures and work environments. The purpose of this paper is to present a critical reflection on the experiences in a faculty P2P mentoring for teaching program and considers the ways in which such programs can influence professional identity formation among TS academics. Design/methodology/approach In this paper, a matched faculty mentorship pair from Nursing and Chemistry disciplines uses critical reflection as a process of inquiry to interpret their experiences of building and sustaining an effective mentoring relationship as part of the P2P program, and to consider implications for professional identity formation and the Scholarship of Teaching and Learning. Findings Through the P2P program, the authors discovered that establishment of clear goals, a commitment to teaching and mentoring processes, and a mutual desire to build a relationship based on authenticity and reciprocity resulted in positive short- and long-term impacts on instructional practices. Professional identity was strengthened through intentional engagement and the opportunity to connect with like-minded peers, contributing to a renewed sense of confidence and commitment. Originality/value Interest in examining professional identity formation in HE has been growing over the past decade. This paper is novel in the critical reflection on a structured peer mentorship initiative through the lens of professional identity formation, with implications for planning and executing mentoring programs for TS faculty.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".