How Principals Manage their Emotions
Bibliographic record
Abstract
This study examines how secondary school principals manage their emotions at work. Specifically, this study used key informant interviews with 13 secondary school principals to identify conditions that lead to emotion-generating situations in their work, and to better understand the strategies they use to manage their emotions. This study also inquired about the supports secondary school principals access to mitigate emotion-generating situations. Gross’ (1998, 2001, 2002, 2010, 2013, 2014) process model for emotional regulation (ER) provides the conceptual framework for this study. The process model includes five families of ER: situation selection, situation modification, attentional deployment, cognitive change, and response modulation. The interview sample included a broad range of principals representing the diverse contexts in which contemporary principals in Ontario work. Several trends emerged when collecting and analyzing the data. For example, principals use several strategies to manage their emotions beyond those previously reported in the literature. Further, participating principals also reported using strategies associated with all five families of ER found in Gross’ process model to manage their emotions. Principals in this study also described facing conditions that can lead to emotion-generating situations, including encountering barriers when advocating for students, system-based challenges, media attention, a lack of support, and managing crises or tragedies in the school community. Although principals in this study did not describe accessing formal professional supports to help them manage their emotions, they did seek out supports within their schools, often through members of their administrative teams. This study and its findings contribute to the current research base on principals and their work, and to the emergent line of inquiry exploring the emotional nature of educational leadership. This study also discusses the implications this research could have for secondary school principals’ professional practice, provincial education policies, and administrative theory. Further, this study also provides a solid foundation for several next steps in the research process, including exploring how ER strategies influence perceptions of principal professionalism, further examining the kinds of situations that incite positive emotional responses, and determining the impact of work intensification on how principals manage their emotions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".