Subversive Educational Leadership: Advancing Excellence, Equity, and Adequacy in Canada, Turkey, and Iran
Bibliographic record
Abstract
Subversion has proved to be a very powerful change force in modern education.This study endeavors to explore the dynamics of subversion within the Canadian, Turkish, and Iranian educational systems with the express purpose of illuminating the potentials of subversion in reordering paradigms.Using a comparative framework, the paper discusses the use of subversive practices across contexts, illustrating which leadership characteristics enable the implementation process.The nuance of subversion for each country can be gleaned clearly while stating what kind of leadership will ensure that subversion is applied for the good of the advance in education.Drawing on theory and evidence, it also brings to the fore qualities of those leaders who promote change, inclusivity, and navigate sociopolitical complexities.This study will be helpful for policymakers, practitioners, and scholars in promoting innovative reform approaches that will assist in achieving excellence, equity, and adequacy in education across the world through the transformative power of subversive leadership. Literature ReviewIn the literature review section, five salient themes of subversion, excellence, equity, adequacy, and transformational leadership will be critically deconstructed.These are core aspects of the educational discourse and provide focal points for extensive inquiry and debate.Subversion becomes a signifying theme of intentional disruption and shifting of well-settled principles and structures in education.In other words, it means that educators are under the compulsion to critically question dominant practices and relations of power so that an environment facilitating creativity and social development emerges.The twin ideas of excellence and equity occupy equal importance as guiding values, both silently embodying the twin mandate of ensuring quality education while advocating inclusivity and fairness.Adequacy further underlines the resource and support needs that are critical to making worthwhile learning opportunities for all learners possible.Finally, transformational leadership comes across as an ethos that can underline the vital contribution that is needed from inspiring leaders in bringing about positive change and leading education
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".