Bibliographic record
Abstract
Although school leaders receive coursework and some practicum experience, there are gaps in their preparation that can only be filled on the job. Because the decisions made by new educational leaders are of great consequence to themselves and their school communities, an important goal should be the sharing of knowledge and support amongst a community of peers. This work reviews the challenges facing new administrators, critically reviews the training of educational administrators in Ontario, and recommends an in-service community method to supplement the support received by new administrators in their first several years. This document begins with an examination of relevant research literature in leadership development, online communities, the nature of expertise, and technology-enhanced learning with technology. One outcome of this review is a set of “knowledge dimensions” that are important to the development of leadership expertise. The dissertation then examines a three year journey of an online community of educational administrators who share in their journey toward expertise. The e-mails from the community were analyzed according to their function within the community and their relevant domain content. Of particular interest was the question of how such e-mail exchanges allowed members to develop in all five dimensions of school leadership knowledge. A coding of e-mail threads revealed that all dimensions of leadership knowledge were represented in the content, and that the quality of e-mails improved in both content as well as knowledge building practices over the three years. The growth of the community as a whole and of individual members is examined through a set of individual case studies. Finally, the dissertation closes with a discussion of the future of this community, as well as the prospects that such an approach could be applied more widely in support of new school leaders.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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".