A grounded theory of educational leadership development using generative dialogue
Bibliographic record
Abstract
The aim of this research was to develop a grounded theory of educational leadership development, using generative dialogue (GD), as an approach to initiating and maintaining professional growth in school principals/vice principals in an urban school district in a relatively affluent region of Western Canada. In Wave I, GD interviews were conducted by a team of consultants, and anonymous data were voluntarily submitted to the research team (n = 37). In Wave II, confidential, one-on-one, audio-recorded virtual interviews were conducted with five participants. Data were transcribed and analysed using grounded theory. The grounded theory model integrated the findings from Wave I and Wave II. There were three final overarching themes: environment, relationships, and leadership. Professional growth was evident when a GD approach was used that emphasized both positive communication and self-reflection. Relationships were supported by a focus on safety and reflected honesty, which led to a positive school culture, while leadership was facilitated through supports and the supervisor role and led to improved accountability. In conclusion, GD serves a useful purpose for facilitating professional growth in educational leaders, but should be supplemented with other evidence-based approaches to meet school leaders’ broader professional development needs, and goals of school improvement. Applicability and limitations of the study are discussed.
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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 | 0.028 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".