Indigenous land-based practices for climate crisis adaptions
Bibliographic record
Abstract
Indigenous communities across Canada persist at the forefront of environmental and climate-related challenges, necessitating a concerted effort to integrate traditional Indigenous land-based knowledge and practices that inherently promote environmental protection and resilience. Using a decolonial feminist theoretical framework, this research centers on Indigenous community perspectives on the climate crisis and their land-based adaptions. Such an approach empowers Indigenous communities to reclaim agency over their narratives and shape research agendas congruent with their lived realities and aspirations. The study concludes by promoting the imperative of revitalizing traditional Indigenous land-based knowledge, practices and relationships with their ancestral lands. Despite emerging recognition within the scientific literature and international agreements, such as the Paris Agreement, of the significance of traditional Indigenous land-based knowledge, many climate mitigation and adaptation initiatives continue to overlook Indigenous participation at various decision-making junctures. Hence, this paper advocates the necessity for international frameworks to acknowledge and integrate traditional knowledge systems and Indigenous participation across national borders, fostering inclusive climate crisis solutions that resonate with Indigenous communities' perspectives and experiences.
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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 | 0.001 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".