Work Intensification and Other Factors and Forces That Heighten Emotions in School-Level Leaders' Work
Bibliographic record
Abstract
Abstract Several factors and forces in school-level leaders' work can heighten emotions and incite emotionally charged situations. Challenges that heighten school-level leaders' emotions are related to systemic factors, people factors and personal factors. The extent to which each of these different factors influence the emotional experiences of school-level leaders, and whether that influence ends up being positive, negative or neutral, is contextual in nature. The systemic factors include encountering barriers when advocating for students, managing an intensified and expanding workload, working within disorienting policy contexts, and receiving a lack of support from their employer. Changes in school-level leaders' work and workload due to the COVID-19 pandemic that heightened emotions and emotional labour are also considered when discussing the systemic factors. People factors evident in the literature include workplace conflict, gendered power relations and crises and tragedies in the school community. The emotional labour inherent in school-level leadership comes to the forefront when considering the impact of these people factors on emotions at work because school-level leaders are tasked with making decisions that can have an immense impact on peoples' lives. Personal factors discussed in this chapter surround a school-level leader's individual emotional intelligence abilities and media attention directed towards them.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".