Adopting a two-eyed seeing approach to leadership in public education: encapsulating both Indigenous ways of knowing and western knowledge to meet our commitment to reconciliation
Bibliographic record
Abstract
Educational leaders in Canada have been struggling with developing and maintaining public schooling that would honour Indigenous world views and ways of knowing to support all students. The purpose of this qualitative study was to analyze how educational leaders can alter their leadership practices towards incorporating a two-eyed seeing (Hatcher, Bartlett, Marshall & Marshall, 2009) approach that is grounded in Indigenous world views and ways of knowing. Using a two-eyed seeing approach in educational leadership practices is important, as it would support engaging in culturally balanced practices that respect both Indigenous and non-Indigenous world views and ways of knowing in public education without a strong emphasis on one over the other. The research question that guided this study is: How can a two-eyed seeing approach guide the practices of educational leaders to adjust their epistemology and bring reconciliation to the forefront in Manitoba public education to create culturally safe spaces for all learners? In this qualitative study, narrative inquiry was the methodology adopted through conducting interviews with a focus on storytelling with Indigenous and non-Indigenous educational leaders. The overall aim of this research was to develop a set of recommendations that would assist educational leaders in their everyday leadership practice, guided by a two-eyed seeing approach. As a result, this practice would potentially lead to a shift towards reconciliation and help to build culturally safe spaces in the school system for all students.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".