Emotion matters in educational leadership: examining the unexamined
Bibliographic record
Abstract
This thesis is a concerted attempt to understand the emotional dimension of leaders' and teachers' experiences of educational leadership. It employs an interpretivist perspective using qualitative data gathering methods in two phases. The recollections of 50 Ontario teachers were studied from transcribed interviews about their positive and negative emotional experiences with administrators. In the second phase, over a seven-month period, 25 school leaders from New Zealand, Australia, the United States, Canada, England and Ireland logged into an anonymous, asynchronous, private, online forum to discuss the emotional dimension of their leadership experiences. Findings from the teachers' phase of the study were shared with the online leaders, providing a direct link between the two data sources. All data were analyzed using grounded theory techniques and where sample sizes would allow, were submitted to a supplementary statistical analysis for further descriptive and interpretive purposes. For the teacher data, the first level of analysis considers domains of convergence and concern: career, students, leadership style/type and emotional climate, organizational procedures, colleagues and parents. The second level of analysis considers leader provocations in conjunction with teachers' emotional inferences and generated three categories: respect, care and professional support. The third level of analysis considers communication patterns: medium and level of engagement. The findings help to reveal the causes, configurations and consequences of leaders' emotional significance in teachers' working lives. The leader data are considered under two broad categories: ‘leaders and others’ and ‘leaders and the self’. Patterns of conflicting pressures from a variety of sources put strains on the leaders' self and are associated with both the problems and the positive potential in relationships with teachers and others. Additionally, the online experience had transformational effects for the leaders. An incipient theoretical framework of emotional epistemologies is proposed. The thesis underscores the emotional and professional value of creating safe spaces for leaders to consider together, the emotional dimension of their work. Implications for theory, research, policy and practice 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".