Becoming a transformative vice-principal in culturally and linguistically rich diverse schools
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the leadership role(s) of vice-principals in diverse, multi-ethnic schools and communities and understand the supervision and mentoring support they require to help them become more effective leaders within them. The research questions guiding this study were: what forms of mentoring do vice-principals, who serve in diverse schools in rapidly changing communities, require? Who is in the best position to provide mentoring for them? Design/methodology/approach The author used a qualitative case study methodology. Data that were analyzed for this paper were drawn from surveys, semi-structured interviews, one focus group interview and school and community documents from three data sets within two case studies in Canada. The first data set was part of the author’s doctoral research program in a diverse school in Alberta; the other case study was part of a larger collective case study that the author is currently involved with and leading in New Brunswick. Several vice-principals were part of both studies. The author then constructed a survey questionnaire specifically focused on mentoring vice-principals in diverse schools. Vice-principals in both provinces, who were part of the two studies, were invited to respond to the follow-up survey. Using a constant comparative analytical approach, the author coded and analyzed the data from all three sets together. The author formed several categories and ultimately collapsed the categories into five distinct themes that illustrated and confirmed the social realities of the vice-principals in their schools and communities. Findings Five key findings emerged from the analysis of the data sets. They were building leadership capacity, fostering positive relationships, increasing global awareness, reducing stress and anxiety and becoming a diversity champion and peace-builder. Originality/value To this researcher’s knowledge, this paper contributes to a significant gap in the literature on vice-principals who serve in diverse schools and communities.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".