Duties as Assigned: How Principals' Leadership Practices Influence their Vice-principals' Leadership Self-efficacy
Bibliographic record
Abstract
This study examines the potentially critical role elementary school principals play in the leadership development of their vice-principals. A growing body of research supports the important role principals might assume in the leadership development of vice-principals. Reports from the field, however, indicate that the vice-principalship does not adequately prepare vice-principals to become school principals. This research attempts to resolve this apparent tension by asking: “How do principals’ leadership practices influence the leadership self-efficacy of elementary vice-principals?” Using self-efficacy as an indicator of vice-principals’ leadership development, this research examines the effects of specific leadership practices by elementary school principals (aligned with the four sources of self-efficacy: enactive mastery, vicarious experience, verbal feedback, and physiological states) on vice-principals’ leadership development in three domains: management, instructional leadership, and moral leadership. This study adopts a mixed methods, sequential explanatory, approach. An online survey of vice-principals in English school districts in Ontario was conducted. Then, 15 interviews of purposively selected participants (from the online survey) were completed based on the results of the initial phase of the research. The findings of the study illustrate a small but significant effect of principals’ leadership practices on vice-principals’ leadership self-efficacy. A strong relationship between principals’ leadership practices and vice-principals’ opportunities to lead within their schools suggests that principals’ leadership practices may also capture a certain style of leadership that is conducive to vice-principals’ leadership development. The qualitative phase of this research suggests that these elements are: autonomy, trust, collaboration, and respect. How these elements influence the working environment and relationship of vice-principals should be considered in future research. This research supports the importance of self-efficacy when considering leadership development in Ontario schools, both as a potential measure of leadership effectiveness and as a means of improving leadership performance in schools, particularly as formal school leadership influences school performance and student achievement.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".