Bibliographic record
Abstract
Abstract The large role that emotions play in the work lives of school-level leaders is absent from much of the research exploring what they do on a daily basis. This chapter discusses future directions for research surrounding the emotional aspects of school-level leadership and how this research can influence practice in meaningful ways. For example, it may be beneficial for future research to focus on principals who are struggling to manage their emotions. This would allow researchers to identify factors or practices associated with school-level leaders who are less able to manage their emotions in a positive manner and provide supports. Further, more large-scale research surrounding school-level leadership is needed to better understand how the ability to manage emotions intersects with other challenges in contemporary principals' work. Research of this nature would also provide additional avenues of support for current leaders. Future directions for practice include an emphasis on changing the culture so that school-level leaders who are struggling feel empowered to reach out beyond their immediate colleagues for supports. Without a renewed appreciation for the emotional aspects of their work, principals, vice-principals and other school-level leaders will be unable to maximise their impact on student outcomes.
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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 | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.012 |
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".