Indigenous women-led climate crisis solutions from decolonial feminist perspectives in Western Canada
Bibliographic record
Abstract
This paper critically explores Indigenous women-led climate change solutions through an anti-racist lens. Indigenous communities, particularly women, have long been disproportionately affected by the adverse impacts of climate change. However, they also possess invaluable knowledge and resilience rooted in their deep connection to the land and environment. Centering Indigenous women's voices and experiences, this reflection aims to shed light on their innovative strategies, highlighting the importance of acknowledging and countering their intersecting oppressions. Following decolonial and relational theoretical frameworks, we learned that Indigenous women's leadership and traditional land-based knowledge offer unique perspectives and solutions for mitigating and adapting to climate change. It emphasizes the importance of building respectful and reciprocal relationships, actively listening to Indigenous voices, and amplifying their calls for justice and equity. Indigenous women helped us to learn how to challenge systemic injustices and work towards collaborative, inclusive, and sustainable climate solutions that center Indigenous women's knowledge, leadership, and self-determination. We can forge a path toward a more just and resilient future for all by uplifting Indigenous voices. • Exploring through the land-based lens, illuminating the longstanding disproportionate effects of climate change. • Centering Indigenous women's voices and experiences reveals their invaluable knowledge and innovative strategies. • B uilding respectful relationships, and challenging systemic injustices for sustainable climate solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.045 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".