Expanding enabling places theory through land-based cultural learning with indigenous men
Bibliographic record
Abstract
• Urban Indigenous men face distinct challenges that effect their wellbeing. • Cultural programming promotes healing but suffers from inconsistent funding and support. • This research combines Indigenous health geographies, Duff’s Enabling Places Model, and a participatory project with an Indigenous men’s group. • Findings expand Duff’s enabling places model with attention to knowledge-place relations. Urban Indigenous men often lack opportunities to connect with the land and their culture. In this article, we report on a community-based participatory research project with urban Indigenous men in a land-based traditional drum-making program in Manitoba, Canada. Using a two-eyed seeing approach that brings together Indigenous ways of knowing and the concept of enabling places from health geography, we examine how land-based programming contributed to the men’s experiences of well-being. Data were collected through Sharing Circles and individual interviews. Two researchers and a research assistant completed inductive narrative analysis of verbatim transcripts using NVIVO software. We found that access to Land was central to men’s relationship-building and sharing of traditional knowledge. The Land supported feelings of energy, awe, and belonging as well as a sense of purpose. Indigenous men’s experiences highlight the importance of land-based knowledge as an enabling resource alongside the material, social and affective resources that have long been recognized as essential in Western models of well-being. This manuscript contributes to the literature on enabling places by bridging Duff’s theory with Indigenous ways of knowing, which emphasize the role of knowledge sharing in the wellbeing of urban Indigenous men.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| 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".