Educational transformation through ethical relationality in education change networks
Bibliographic record
Abstract
By bringing together Indigenous and non-Indigenous researchers, educators, and community partners, we investigated whether and how education change networks (ECNs) can support educators to access and collaborate with Indigenous community partners to transform teaching and learning in classrooms and expand mainstream notions of success through reconciliation Indigenization (Gaudry & Lorenz, Citation2018). This critical case study was guided by Indigenous Storywork principles (Archibald, Citation2008). We engaged in respectful and responsible data collection, analysis, and knowledge mobilization approaches to position local Indigenous Knowledge Holders as curriculum development and educational change partners. Study findings demonstrate how ECNs can support reconciliation Indigenization when K-12 educators collaborate with Indigenous community partners to welcome local Indigenous Knowledges and epistemologies into schools and classrooms, transforming teaching and learning through local Indigenous story telling, land-based, and strength-based practices. This study also contributes empirical research showing how ECNs can provide opportunities for K-12 educators and Indigenous community members to enact decolonial Indigenization (Gaudry & Lorenz, Citation2018) by intentionally identifying, challenging, and dismantling colonial practices and structures, and interrogating and unlearning colonial ideologies. This study suggests that a central aspect of ECNs for community members is ongoing explicit work towards developing ethical relationality within spaces of engagement. This study also contributes methodologically by demonstrating the decolonizing potential of research partnerships between Indigenous and settler scholars, educators, and communities, which strengthens mainstream methodologies and places them in alignment with decolonization-focused goals, activities, and methodologies.
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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 | 0.014 | 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 | 0.018 | 0.048 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".