I Do as I Think: Exploring the Alignment of Principal Cognitions and Behaviors and Its Effects on Teacher Outcomes
Bibliographic record
Abstract
Purpose: Decades of research in educational leadership has extolled the importance of principals in both the effective functioning of teachers and the overall performance of schools. The mechanisms explaining what makes principals effective in the eyes of teachers, however, are not well known. This study builds on prior research to posit that principal effectiveness is the result of their own cognitions, which are translated into specific leadership behaviors directed at teachers, which, in turn, serve to foster trust. Research Methods/Approach: Data came from principals and teachers in 33 French and English Canadian elementary schools. All principals took part in a face-to-face interview which elicited their leadership cognitions, while teachers responded to measures of principal leadership behaviors, trust, and effectiveness. Findings: This research provides empirical support for a sequential mediation model whereby principal leadership cognitions were related to teacher evaluations of principal effectiveness through teacher ratings of principal leadership behaviors and teacher trust in their principal. Specifically, we found support for three specific leadership behaviors (supporting, developing, and active management-by-exception). Implications for Research and Practice: The results reveal the importance of a number of distinctive leadership behaviors in fostering a work context where teachers are able to perform effectively. Principals who “walk the talk” tended to be perceived as more trustworthy and effective. Additionally, the results suggest that principals’ professional development could focus on cognitions related to leadership behaviors and effectiveness.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".