Theoretical Foundations: Affect and Emotions in Educational Administration
Bibliographic record
Abstract
Abstract This chapter aims to provide a comprehensive overview of the ways in which emotions, emotional regulation and emotional labour are treated by various theoretical perspectives that influence the work of school-level leaders. Notions of professionalism and the scientific/rationalist roots of administrative theory (e.g., Taylor, 1911) relegated emotions and other subjective leadership qualities to the sidelines of the seminal debates in the educational administration field. That has changed since the turn of the twenty-first century as scholars have begun to acknowledge that the very nature of school-level leadership involves emotional components and success as a school-level leader demands effective emotional regulation. The chapter includes an evidence-based analysis of the role(s) that emotions, emotional regulation and emotional labour play in school-level leaders' work. I also discuss two broad categories of theoretical perspectives surrounding the emotional aspects of school-level leadership. The first primarily considers how school-level leaders personally experience emotional phenomenon in schools, while the second prioritizes how socio-contextual elements that influence their emotional experiences in the workplace. I conclude the chapter by discussing how school-level leaders manage emotions they experience as part of their job is an important piece missing from the models and frameworks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".