Decolonizing Climate Research and Policy: making space to tell our own stories, in our own ways
Bibliographic record
Abstract
Abstract As the climate crisis deepens, the discourse aimed at finding solutions continues to intensify. Increasingly, government agencies and world leaders are recognizing the critical importance of including Indigenous knowledge and perspectives in the development of sustainable responses. To date, however, opportunities for meaningful Indigenous engagement in the creation and development of climate solutions remain few and far between. Where such opportunities do exist, they are most often designed with the intention of trying to `incorporate' limited elements of Indigenous thinking into an established non-Indigenous framework. This process of `Indigenizing' mainstream initiatives achieves little if anything in the way of deconstructing the colonial world view that has caused the climate crisis in the first place. Actively decolonizing Canada's and the world's responses to climate change is absolutely necessary if global society is to achieve the `transformation' of our economies and societies deemed essential for our survival by the United Nations Intergovernmental Panel on Climate Change (IPCC), among others. No longer can we afford to simply `tweak the system' by slotting into mainstream processes those elements of Indigenous world view that seem to `fit' most comfortably. If we continue to value the colonial system of neoliberalism and runaway capitalism over those proven sustainable paradigms wherein respect for all life is paramount, then no lasting solutions are possible. Instead, we need to `flip the script', and start providing spaces wherein Indigenous thinking and world view provide the fundamental framework, and non-Indigenous ideas that align sufficiently with Indigenous principles may be incorporated as appropriate. This article recounts a panel discussion, hosted by Indigenous Climate Action, in which four influential Indigenous climate researchers, organizers and policy experts share their reflections on how climate research and policy can be actively decolonized. The panelists draw on their experiences to engage and encourage the audience, listeners, and readers to explore ways to decolonize their own perspectives and to support climate solutions inclusive of all People.
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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 | 0.079 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.147 |
| Scholarly communication | 0.038 | 0.054 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.015 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".