“I see my culture starting to disappear”: Anishinaabe perspectives on the socioecological impacts of climate change and future research needs
Bibliographic record
Abstract
Climate change disproportionately affects Indigenous Peoples because of strong connections between environmental, cultural, and spiritual well-being. While much of the global discourse surrounding climate change is founded in Western science, the holistic, place-based knowledge of Indigenous Peoples offers a complementary way of understanding and mitigating climate change impacts. The goal of this research was to elevate Anishinaabe concerns, observations, and perspectives about climate change impacts and future research needs. We organized a workshop called “Connecting Guardians in a Changing World” where participants shared concerns about animal and plant life cycles, water cycles and water quality, and impacts to ways of life, including reduced capacity to perform cultural practices and erosion of their knowledge. Participants highlighted the challenge of prioritizing a single impact of climate change, emphasizing that impacts to the environment and ways of life are interconnected. Participants also expressed the need for research and policy that move beyond interdisciplinarity to include intercultural philosophy and research that better reflects Indigenous worldviews and incorporates Indigenous methodologies. Moving forward, meaningful partnerships and opportunities for knowledge sharing should be prioritized in climate change discourse to ensure solutions are generated together, with all of the tools and knowledge available.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.031 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".