Finding fit in friction: the value of contrast in mentoring for leadership development
Bibliographic record
Abstract
Purpose The dual purpose of this paper is (1) to describe and contextualize encounters between mentors' and mentees' differing needs in a leadership development programme and (2) to posit that practice negotiating frictional encounters constructs “good fit” between mentors and mentees and is a potentially important skill for leadership development. Design/methodology/approach The authors gathered data through qualitative, semi-structured interviews of mentors, mentees and mentoring programme staff participating in a mentoring programme for leadership development offered at a mid-sized Canadian business school. Using a grounded theory, interpretive analytical approach, the authors examine the notion of “good fit” and how it emerged in encounters between participants' diverse needs. Findings The authors identified participants' mentoring needs by eliciting their experiences of “good fit” in the focal leadership development programme. The findings revealed that encounters between contrasting needs fell into two categories: (1) the need for career advising versus leadership development and (2) the need for structured versus free-flowing conversation. Those encounters, in turn, generated opportunities for leadership development. Practical implications The findings have valuable implications for designing mentoring for leadership programmes. Namely, the authors propose pairing individuals with similar deeper-level qualities but diverse educational backgrounds and experiences to allow for practice in negotiating encounters with friction and contrast. Originality/value As an empirical study of mentoring for leadership development in practice, this study applies a dialectical approach to encounters across contrasting mentoring needs. In doing so, it locates leadership development potential in those frictional encounters.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.003 | 0.005 |
| 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".