When Good Intentions Aren't Good Enough: Dismantling Colonial Praxis in Educational Leadership
Bibliographic record
Abstract
Addressing the inequitable educational outcomes of Indigenous students in K-12 education in Alberta is a moral imperative, a mandate of the provincial government and a response to Call to Action #10 of the Truth and Reconciliation Commission. This dissertation-in-practice, viewed through the lens of critical theory, interrogates systemic barriers within Three Bear Hills School Division (a pseudonym) that hinder Indigenous students' graduation and post-secondary transition rates. Indigenous students in K-12 education are adversely impacted by the influence of settler colonialism on leadership praxis, as evidenced by the achievement gap discourse, deficit thinking, and unconscious bias. These factors lead to the over-representation of Indigenous students in lower academic streams and special education classes. Historical efforts to address these issues through decolonizing reforms at provincial and national levels have been largely unsuccessful. This dissertation-in-practice employs transformative and adaptive leadership approaches, utilizing Stroh’s 4-stage change model to dismantle leadership’s colonial mental models. Education partners collaboratively create a shared vision of a preferred future using an appreciative inquiry methodology. Transformative learning theory supports third-order change among school leaders through reflexivity and dialogic practices. Third-order change, grounded in the decolonization of leadership, must precede second-order decolonizing practices such as land-based learning and language programming to achieve equitable educational outcomes for Indigenous students. Through personal transformation, educational leaders can reconsider their mental models and develop critical consciousness, paving the way for meaningful, systemic change.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.055 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.010 |
| 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".