Bibliographic record
Abstract
Abstract Healthy and adaptive strategies for regulating emotions and coping with the demands of their jobs can help school-level leaders mitigate the factors and forces heightening the emotional aspects of their work, stave off the negative effects of work intensification and achieve wellness. As with most individuals in most professions, school-level leaders use several different strategies to manage their emotions and cope with the stresses associated with their work. Some of these coping strategies are associated with positive outcomes including situation selection and exercising autonomy over their workday, talking to colleagues, reappraisal, humour, controlled breathing, exercise and engaging in hobbies outside of work. However, even the most experienced and effective school-level leaders demonstrate a proclivity for engaging in coping strategies associated with maladaptive and negative outcomes. These maladaptive strategies include worrying about events over which they have little or no control, masking one's emotions using expressive suppression, using thought suppression to deal with symptoms of emotional exhaustion, distraction, manipulating the emotions of others as well as use of illegal or prescription drugs, alcohol and other forms of self-medication. This chapter concludes with a discussion of how there can be some overlap between these strategies in practice and how they are classified.
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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 | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".