A grounded theory of educational leadership development using generative dialogue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it