<i> <i>Akiikaa</i> </i> (it is the land): exploring land-based experiences with university students in Ontario
Bibliographic record
Abstract
Indigenous learning traditionally comes from the land. Akiikaa ('it is the land' in Algonkian) is designed to assist graduate students in thinking beyond the classroom and understanding the elements of life as known by Indigenous people to live a healthy life. Akiikaa will provide graduate students (both Indigenous and non-Indigenous) with opportunities to learn about Indigenous ways of knowing. They will learn from an instructor, Elders and their peers about how the land is an instrumental part of all aspects of Indigenous life including health and well-being. One of the goals of the Master of Public Health in Indigenous Health program (at the University of Toronto) and the land-based experiences is to shift the thinking away from humans being the dominating force on Mother Earth to equality amongst all aspects of life. Graduate students are introduced to 'personhood' rights for plants, animals, water, and air, which is a shift from the current World Health Organization's view of public health that builds upon a population health approach but neglects the elements that surround humans as necessary for living a healthy life. It has been suggested that Indigenous land-based education acts as a method of decolonization through reclamation of Indigenous ideology and use of land. Land and land experience are highly prized by Indigenous people around the world as cultures and languages are based on the interaction of people with nature/land. This move to delivering the curriculum in the natural environment using Indigenous knowledges as its pedagogy is anticipated to change attitudes about Indigenous people and issues as well as improving the health and well-being of graduate students and, over time, Indigenous peoples' health and well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".