New Principals Leading Change: Disrupting the Status Quo through Critically Conscious Principal Reciprocal Mentoring
Bibliographic record
Abstract
This dissertation focused on the extent to which principal mentoring supported new principals’ leadership for equitable outcomes for all students. Using a human-centered leadership framework, three principal mentoring pairs participated in a two-month study of their mentoring practices. A combination of individual and group interviews was used to explore the kinds of mentoring practices that were used and their impact on decision making and leadership in three Canadian elementary schools. Findings included the importance of social-emotional leadership. New principals needed encouragement and support as they led their schools through the COVID-19 pandemic. Understanding and awareness of current bias did not shift through conversations between mentors and mentees. Mentors and mentees engaged in storytelling and rehashing one’s “aha moments.” These past reflections affirmed existing assumptions and did not appear to shift mentees’ mindsets or lead to resolutions for practice in future dilemmas. Mentors did not report discourse patterns that challenged existing practice or offered insight into tactics that might leverage participatory decision-making for long-term change. The results suggest the need for a structured principal mentoring program that focuses on leadership for equity. This approach to mentoring can be taught.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".