Rethinking Sustainability through Indigenous Language Futures
Bibliographic record
Abstract
Language experts estimate that 50 to 90 percent of the world’s languages will become extinct by the end of this century. The great majority of those “endangered” languages are Indigenous. After 500 years of colonial erasures of Indigenous languages, the silence of ancestral voices in their landscapes is a tragedy of global proportions. Entangled in Indigenous language loss are the transformations of Indigenous worlds into alien landscapes. The colonial introduction of invasive species of plants, animals, pathogens, and microbes, as well as ideational concepts and ideas, have rendered traditional practices unsustainable in colonial worlds. Despite these upheavals, Indigenous communities are reinscribing and rearticulating their ancestral voices in multimodal worldmaking projects that celebrate resilience but also establish foundations for their futures. The success of these projects will depend upon rethinking the terms of sustainability. Each day seems to bring news of the latest horrors of climate change due to extractive industries, rapacious capitalism, and the slow violence of settler-colonialism. Alienation of the landscape is no longer just an Indigenous concern; rather, it has become a reality for all populations. Past practices of sustainability have contributed to the current conditions of climate change. As the horrors of global warming and disruptions to human health and safety spread to wealthy populations, rethinking sustainability is urgently needed. Perhaps, it is time to rethink sustainability not in colonial terms but in Indigenous terms. This chapter is about the eradication of Indigenous sustainable and adaptive systems of environmental stewardship and how contemporary self-delusional fantasies of development and wealth are accelerating the rush toward human species suicide.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".