Bibliographic record
Abstract
Educational institutions, and in particular educational leaders, play critical roles in identifying and rectifying the many inequities that oppress, marginalize, and exclude individual students, educational actors, and some minoritized groups in Canadian education.Leading for Equity and Social Justice provides a deep look at some of these inequities and injustices and offers transformative leadership as one way for leaders to stimulate, support, and foster equitable and socially just practices in educational institutions.This collection emphasizes the systemic nature of inequality and supports the necessity of systemic change to target not only individuals but also structures, policies, and far-reaching practices.Focusing on various marginalized groups -including the Indigenous community, LGBTQ2S+ peoples, refugees, newcomers, and specific groups of teacherschapters explore transformative leadership in practice and how to achieve inclusion, respect, and excellence in schools.Arguing that leadership involves much more than simply putting policy into practice, Leading for Equity and Social Justice promotes the need for leaders to recognize their role as advocates and activists.andréanne gélinas-proulx is a professor of educational administration at
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.897 | 0.800 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".