Working towards relational accountability in education change networks through local indigenous ways of knowing and being
Bibliographic record
Abstract
Indigenous communities and students have been marginalized by colonial practices, disproportionally referred to special education programs, and encounter systematic prejudice and discrimination in education systems that lack respect for their ways of knowing and being. To disrupt hierarchical practices and structures that enact a hidden curriculum of privilege and racism, reconciliation and educational and system transformation need to work in tandem. Drawing on critical case study guided by Indigenous Storywork principles, we are researching how Professional Learning Networks (PLNs) can support educators and Indigenous community partners’ collaboration to decentre colonizing education practices. Analysis of preliminary data offers a window into the potential and complexity of engaging in decolonizing work that asks educators to unpack their role in reconciliation efforts and unlearn much of what they believed to be ethical practice. Findings include: participants awakening to structural inequities and racism; white/settler participants engaging with difficult knowledge; educators emphasizing their need for external resources to decolonize their practice; and a delicate balance between educators feeling challenged, feeling hopeful, and recognizing the distance yet to be travelled. This study demonstrates that collaboration with Indigenous community partners within education change networks (ECNs) holds potential to support pedagogical transformation and ultimately redefine student success.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".