Understanding decolonial learning in the climate justice movement : a decolonial feminist autoethnography
Bibliographic record
Abstract
In 2016 I took to the streets with thousands of others to march in opposition to the proposed Trans Mountain oil pipeline, a project intended to carry bitumen from the Alberta Tar Sands to the west coast of Canada. The pipeline has been met with sustained opposition from climate and environmental justice activists, as well as Indigenous communities fighting the project on the basis of Indigenous rights and sovereignty. Not long after becoming involved, I began to think about the social movement as a pedagogical space. As climate and environmental justice activists work with Indigenous communities in opposition to the fossil fuel industry, what are we learning? Specifically, what are activists and organizers learning about colonization and decolonization? Is decolonial learning taking place in Indigenous led opposition to the fossil fuel industry? If so, how? My research explores the decolonial learning that is taking place as climate justice activists work in solidarity with Indigenous communities. As a white, non-Indigenous woman, I used decolonial feminist theory to position myself in relation to the project. From this theoretical grounding, I relied on critical autoethnographic methods. The result is a critical autoethnographic story — a weaving of my own experience with the experiences of other activists and organizers as we learn to decolonize mind-spirit-body in and through social action with Indigenous communities. The narrative begins with an introduction to the movement and an invitation into a community where learning is taking place. It then explores decolonial learning — a process of unsettling ourselves in order to understand the historic and continued colonization of both Indigenous peoples and the Earth. Decolonial learning is a messy process of unlearning colonial habits of mind, body and spirit and replacing coloniality with new ways of thinking, feeling, being and doing. When we have begun to do the work of decolonial learning, we can better show up in decolonizing solidarity, a rich place to learn decolonial alternatives. Ultimately, this autoethnography tells the story of both my own and other activists’ experiences as we begin to unlearn coloniality, learn to imagine decolonial futures, and more importantly how to act toward them.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".