Bibliographic record
Abstract
Abstract School-level leaders are suffering. They are experiencing a well-being crisis after years of working long work hours, managing an unrelenting workload, and navigating shifting policy contexts. School leaders that are experiencing work intensification and highly stressful work environments can suffer personal and professional consequences. On a professional level, school-level leaders are immersed in intensified work environments that mute the power and potential increasingly associated with their role. This chapter describes challenges that threaten school-level leaders' productivity, job satisfaction, happiness and well-being, such as work intensification, burnout, stress as well as loneliness and isolation. In these ways, the emotional aspects of their work can influence school-level leaders' ability to lead happy and healthy lives, while also having the supplementary effect of making the position less attractive for the next generation of school leaders. Research conducted in jurisdictions around the world is discussed throughout the chapter to demonstrate that the well-being crisis experienced by school leaders is an international phenomenon. I also use this discussion of the challenges facing contemporary school-level leaders to encourage them to reconnect with the reasons why, and the emotions they felt, when first pursuing a position in K-12 school-level leadership.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".