Ontario school principals and diversity: are they prepared to lead for equity?
Bibliographic record
Abstract
Purpose Ontario is the most ethnically diverse province in Canada. School educators cannot disregard the reality of diversity in all its senses. The question that directs the focus of this paper is: to what extent are leaders in Ontario formally prepared to lead schools that support the students of today? The paper aims to discuss this issue. Design/methodology/approach This paper results from a document analysis of publicly available documents to investigate the current context in Ontario, Canada regarding the educational and social situation of new immigrants, immigration, refugees, and ethno-culturally diverse students. It includes an exploration of the training and information available for principals and vice principals in Ontario to support them in leading schools which serve increasingly diverse student bodies. This paper is also informed by interviews with 59 educational leaders from Ontario and the USA. Interviews centred around their experiences engaging in practices to support their diverse student populations. Findings Results of the data analysis regarding the supports and training available to assist educational leaders in supporting the learning needs of diverse student populations demonstrate a serious lack of support. Very few actual resources exist for leaders, and most of those that do exist are connected to agencies outside of the education system, such as Settlement services. Research limitations/implications Recent research investigating the relationship between identities and educational experience and teaching points to the importance of in-depth and targeted approaches to teacher preparation, and leadership preparation to help provide teachers and leaders with the knowledge, dispositions, and skills required to support all students in their academic and social development (Cochran Smith et al., 2009). This study confirms such findings. Given the current situation in Ontario and much of North America, there is a great need for leadership preparation programming, and school-level resources that provide educational leaders with the knowledge and skills to support all students in their academic and social development, but also in helping educational leaders to understand and address the systemic nature and forms of discrimination which their students face daily. Practical implications With very few school boards requiring leaders to have a Masters’ degree in Education, and none having stipulations regarding course content. This leaves educational leaders unprepared to deal with the reality of their situations, leading to the perpetuation of inequities in Ontario schools. Originality/value Although sparse, previous research into the state of support and preparation for administrators and educators with regard to diverse student populations in Ontario revealed that little to no preparation or support exists (see e.g. Price, 2002). This study helps to fill that gap in existing research.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".