Doing leadership development through mentoring in a social learning space: the case of the inaugural Leadership Learning Lab
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe and contextualize the processes of leadership development through mentoring in a Leadership Learning Lab (“the Lab”) and to explore the implications and applications of the Lab's approach as a social learning space. Design/methodology/approach The authors used a constructivist grounded theoretical approach and conducted semi-structured interviews with participants in the Lab, which operated out of a leadership center in a mid-sized Canadian business school. Findings The findings show that participants used their individual life experiences to practice leadership development through mentoring in a social learning space of prescribed uncertainty. The participants identified with becoming flexible, self-actualized leaders by learning to view their own experiences and those of their Lab partners through a leadership lens. Originality/value This study contributes to an understanding of the “doing” of leadership development in a social learning space and highlights three relational processes through which leadership development emerged through mentoring: rapport-building, democratization and reflection.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".