Literary Responses to Indigenous Climate Justice and the Canadian Settler-State
Bibliographic record
Abstract
Within a context of cultural- and land-based Indigenous resurgence, contemporary Indigenous writers, artists, theorists, and activists have made the settler-state and extraction economy of Canada a flashpoint of the global climate emergency. Indigenous peoples often exist on the front lines of climate change, finding their lives and livelihoods threatened by the effects of rising temperatures even as they have been excluded from many of the benefits afforded by carbon-intensive economies. This chapter examines how Indigenous writers place climate change within a long, ongoing history of colonial resource appropriations, ecological loss, and violent suppression of Indigenous bodies and cultures in Canada. The chapter also addresses the diverse ways they respond to its challenges, including: crafting texts and practices of political dissent, solidarity-building, and land reoccupation; grounding present experiences in enduring stories of Indigenous response to environmental and political change; and refashioning genres such as science fiction, horror, or post-apocalyptic imaginaries to explore Indigenous futurisms in a climate-altered world. Above all, Indigenous writers make clear that climate change cannot be extricated from decolonisation and matters of sovereignty. The restoration of Indigenous lands and land-based ways of knowing is the starting point for the pursuit of climate justice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.027 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".