The CARE Model: Reimagining Education through an Emancipatory Framework that Disrupts Coloniality in School Systems
Bibliographic record
Abstract
The rise in neoliberalism, alongside globalization, has created a power imbalance in all aspects of society, including education. For instance, Ontario schools’ participation in the EQAO, fundraising practices, academic streaming, and carrying out of disciplinary measures, pits schools against each other, placing some schools in a cycle of prosperity, while others in a vicious cycle of oppression. These oppressive practices espoused by neoliberalism are intricately tied to colonialism and have far-reaching implications on how educators and leaders think, teach, and implement policies concerning poor, and racialized students. The purpose of this report, therefore, is to present a workable model for educational leaders to decolonize school systems and disrupt coloniality from school systems. Drawing on the insights from fifty-one sources, which include both scholarly and grey bodies of work, the author conceptualizes and propose the Challenge, Align, Revive, Embrace (CARE) model to reimagine education that is void of colonial remnants. The strategies and concerns around the implementation of the CARE model for education leaders are also discussed, followed by a call to action for researchers, practitioners, and policymakers to empirically validate the model across diverse contexts. Key words: neoliberalism, post-neoliberalism, colonialism, decolonization, anti-oppressive, EQAO, deficit thinking, culturally sustainable pedagogies, policy-to-practice continuum, authentic partnerships
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.098 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".