Digging Up the Roots of Educational Policy: Curriculum Infusion and Aboriginal Student Identity Development
Bibliographic record
Abstract
Since 2002, Alberta teachers have been required to infuse Aboriginal perspectives into the K-12 curriculum across all subject areas in order to positively impact Aboriginal children’s identity development. There are several assumptions inherent in the policy of infusion that this study uncovers and examines using Cree knowledge and research methods as the foundation of inquiry. The questions that guided the study were threefold. The first task was to understand what Aboriginal identity is and how it develops and functions. Second was to examine what happened to Aboriginal identity to impact its development in Aboriginal people. The final query was to explicate the roles and impacts of Canadian teachers and schools on Aboriginal identity development. Based on the knowledge and understanding of three Cree knowledge holders, this study presents a model of Aboriginal identity as a living entity that grows and develops within a cultural ecosystem. The model is then used as an analytical framework to evaluate the policy of infusion for its potential efficacy in contributing to the development of Aboriginal identity in schools. The study concludes that Aboriginal identity development requires a cultural ecosystem that includes Aboriginal peoples, ceremonies, histories, knowledges, languages, and lands as inherent elements of identity and its development.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".