“A Return <i>to</i> and <i>of</i> the Land”: Indigenous Knowledge and Climate Change Initiatives across the Canadian Prairies
Bibliographic record
Abstract
While research on Indigenous knowledges on climate change is increasing, especially in the Arctic, few studies document Indigenous perspectives on climate change in the Canadian Prairie provinces (Alberta, Saskatchewan, and Manitoba). This paper addresses this gap and follows an Indigenous community-based research approach using semi-structured interviews and participatory video to explore how Indigenous peoples in the Prairies are experiencing and responding to climate change. Ten video interviews were conducted with members of eight communities across the Indigenous territories of Treaties 1, 4, 6, 7, and 8. An integrated video editing and qualitative content analysis approach was conducted and eight short videos were produced. Results show that participants across diverse territories have experienced changes in their environments—attributed to the cumulative impacts of industrial development, climate change, and other influences of colonialism—which have significant impacts on cultural well-being. Communities are also pursuing solutions—such as land-based education, renewable energy, grassroots activism, cross-cultural dialogues, and ecological restoration—which serve to address these socio-ecological challenges. Across these solutions, six common themes emerged: Indigenous leadership; capacity and self-sufficiency; sustainable economic development; sharing Indigenous knowledge; connecting with the land; and bridging Indigenous knowledge and Western science. While it is increasingly recognized as critical to heed Indigenous voices on climate change, this paper makes a significant contribution to understanding the diversity and parallels in the ways in which Indigenous communities are being impacted by and responding to climate change in the Prairie provinces, as well as collaborative and creative methods for sharing these perspectives across cultures and geographies.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.036 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".