Canada’s Indigenous Peoples’ Access to Post-secondary Education: The Spirit of the ‘New Buffalo’
Bibliographic record
Abstract
In this chapter, Ottmann focuses on a people group that contributes to the complexity of the educational landscape, people who are indigenous to the land but often not recognised as such, people that continue to confound many researchers, educators, leaders and policy-makers at all levels of education (elementary, secondary and post-secondary) – Canada’s First Nations, Métis and Inuit peoples. As an Anishinaabe person, the author resides within this circle. Ottmann asks the following questions: Why do significant educational gaps still exist and why do many First Nations, Métis and Inuit students disappear from the halls of our learning institutions, particularly in times of transition (i.e. from grades 6–7, 9–10, and 12–post-secondary)? How can educational leaders and teachers equip themselves to support students who see and experience the world differently – students who, in general, have not been responsive to traditional Eurocentric educational approaches? Do foundational educational precepts (i.e. philosophies, theories, methodologies and strategies) need to change to resolve long-standing issues (i.e. the education gap between Aboriginal and non-Aboriginal students in Canada) to make way for inclusive, innovative, caring and supportive spaces in education? To answer these questions, Ottmann first provides a picture of the educational landscape of Canada’s Indigenous peoples, which includes insight and history of the systemic and systematic barriers, and the worldview differences between Indigenous and non-Indigenous peoples when it comes to the pursuit of post-secondary schooling success. Ottmann then explains the important role that Indigenisation and decolonisation has in strengthening post-secondary institutions and provides examples of access and transitioning programs in Canada, and ends with a case study. In the pursuit of the ‘new buffalo’ (education), the Trickster (transformational character) is at play in terrain defined by constant flux (change). It is by embracing and learning from these key concepts that learning institutions can develop sustainable access and transitioning programs that will not only benefit Indigenous students but all students. Ultimately, what is good for Indigenous students is good for all students. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.027 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".