A Collaborative Multi-Method Approach to Evaluating Indigenous Land-Based Learning With Men
Bibliographic record
Abstract
In Canada, a vast majority of urban Indigenous people face distinct challenges accessing and connecting to Indigenous cultural practices. Research has found that colonial policies and practices continue to disrupt and fracture traditional methods of passing down cultural teachings, including dispossession from traditional lands in which cultural practices are rooted. This disruption continues to affect the availability of educational programming by and with Indigenous people and in Indigenous languages. This research involves a multi-method approach to observe and engage with a land-based traditional drum-making program for Indigenous men in an urban center in Southwestern Manitoba. By participating, watching, and listening to the men within the workshops through unstructured observation, Sharing Circles, individual interviews, and photovoice, we aim to understand the impacts of land-based learning on Indigenous men’s well-being. The study is designed in accordance with University and Tri-Agency ethical guidelines, integrating ownership, control, access and possession (OCAP), as well as the principles of respect, relevance, reciprocity, and responsibility within all phases of the research. The research is co-created by the university researchers, community collaborators, and other relevant stakeholders.
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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 | 0.053 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".