The Ripple Effect: How One Rural School Can Embrace Indigenous Learning on a Journey Towards Truth and Reconciliation
Bibliographic record
Abstract
In a K–9 rural school in Alberta, the lack of opportunities for land-based learning and understanding of Indigenous truths, histories, and ways of knowing creates a significant gap in knowledge that is an ethical obligation to address. For the school to engage in social justice and transformation to address this problem of practice, it is crucial to address this gap and work towards decolonization and indigenization. The goal of this Organizational Improvement Plan is to ensure that staff gain a deep awareness and understanding of the historical oppression and marginalization of Indigenous peoples in Canada due to colonization, both historically and through colonial systems that persist today. This transformational process will require building the critical consciousness of the staff and creating a compassionate learning environment that enables them to engage in this important work. Although the school has a racially homogenous population, it is imperative to take a firm anticolonial stance to address the legacy of colonialism that has been perpetuated in Canada for centuries. The change implementation plan adopts systems thinking to facilitate social change by recognizing the interconnectedness of different parts of the school system. It allows for a comprehensive understanding of the social issue being addressed in the problem of practice. A knowledge mobilization plan is developed to effectively disseminate the insights gained from the implementation to stakeholders and the wider community. By leveraging anticolonial theory and taking a proactive approach to education, staff can build the necessary awareness, attitudes, and actions to support decolonization and indigenization in the school and beyond.\nKeywords: decolonization, indigenization, social justice, anticolonial theory, critical consciousness, systems thinking
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.030 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".