A Call to Action: Indigenizing Curriculum through Adaptive Leadership
Bibliographic record
Abstract
Since the release of the Calls to Action from the Truth and Reconciliation Commission of Canada in 2015, the post-secondary sector has focused their attention on the indigenization of programming and practices with mixed results. This Organizational Improvement Plan (OIP) presents a possible solution to embed Indigenous knowledge and culture in a program offered at a large urban college in Canada.\nBoth decolonization theory and Schein’s cultural assessment (2017) are used to identify current values and structures that are barriers to the effective integration of Indigenous knowledge in course curriculum and teaching. Utilizing an adaptive leadership framework (Hefeitz, 1994, Northouse, 2016) this OIP works to overcome identified obstacles by emphasizing collaboration, learning, and a safe environment that supports faculty in adopting new ways of thinking and working. This OIP focuses on the creation of a collaborative partnership with Indigenous communities (Guenette & Marshall, 2008; Hongyan, 2012; Pete, 2016; Young, Zubrzycki, Green, Jones, Stratton & Bessarab, 2013), mandatory faculty training (Pidgeon, 2016), and the development of a community of practice (CoP) (Ledoux, 2006; Ottmann, 2013) to facilitate a transparent and effective process for the indigenization of courses and teaching. This OIP may provide a model for other institutions working toward the goal of indigenization within their programming.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".