Bibliographic record
Abstract
Abstract Emotions are everywhere and, as multiple scholars have argued, can be considered a fundamental part of the human experience. Individuals are expected to behave in socially appropriate ways in a variety of public and private social situations, which often involve managing one's emotions. The management and regulation of emotions are also key components of effective school leadership. This chapter unpacks the emotional aspects of school leadership by exploring how the management of emotions is fundamental to the success of headmasters, principals, vice-principals, and other school-level leaders. I also provide the rationale for using the term ‘school-level leaders’ and call for emotional authenticity in educational leadership. Then I outline several benefits an increased scholarly and practical focus on the emotional aspects of school-level leadership offers for teachers and other school staff, school-level leaders themselves, and the students they serve on a daily basis. I also provide a short description of the methodology used for the participant quotes used to add richness and contextualize key themes explored in Chapters 4 and 5. The chapter concludes with an outline of how the rest of this book is organized and offers additional insight into the topics explored in the forthcoming chapters.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".