Characteristics and Context: A New Model for School-Level Leaders' Emotional Regulation
Bibliographic record
Abstract
Abstract In this chapter, I propose a model for how school-level leaders manage their emotions. This model consists of six components. School-level leaders typically have little direct influence over the first component of the model, which are the socio-contextual factors in the schools, school communities and jurisdictions in which they work. A school-level leader's identity, sense of self and their personal characteristics comprise the second component of the model. The third component of the model is a multi-directional arrow demonstrating connections and interactions between the socio-contextual factors and a school-level leader's sense of self. Factors that heighten school-level leaders' emotional experiences in schools are considered as part of the fourth component of this model for school-level leaders' emotional regulation. The fifth component are the emotional regulation strategies school-level leaders use to manage emotions that emerge as part of their workday, while influence of supports and professional learning are considered as part of the sixth component, Finally, the model also accounts for the chain reactions and feedback loops that can occur when an individual utilizes an emotional regulation strategy that is unsuccessful. Those processes produce new emotions that must be regulated using similar, or different, emotional regulation strategy(ies).
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".