Leadership in Continuing Education: Leveraging Student-Centred Narratives
Bibliographic record
Abstract
For this study, we interviewed eight Canadian and American continuing education deans and directors to explore how their personal accounts or “stories” about leadership high- light the dynamic nature of their leadership roles. This article focuses on the potential impact of these stories to better integrate and serve the adult learner within the higher education environment. Four major themes emerged from our analysis of the data: the non-traditional career trajectories of the leaders; marginalization and identity; lead- ership and innovation; and alignment and resistance.Our study suggests that continuing education leaders generally excel in sharing student-centered narratives and in pushing boundaries—in part to convince diverse stakeholders of the importance of the field of continuing education. Interviews with participants indicate that continuing educa- tion leaders think in interdisciplinary terms and weave a master narrative about life- long learning, combining several individual threads. Continuing education leaders strive to have conversations leading to collaborative partnerships and educational innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".