“Conquered Primitives Have No Written Language”: Language Revitalization, Reactionary Settler Colonialism, and Perpetual Genocide
Bibliographic record
Abstract
Indigenous people in settler colonies such as Australia, the United States, and Canada are currently engaged in a range of projects to revitalize their languages: to reclaim and restore them in the wake of colonial destruction. Such language revitalization is frequently met with fierce backlash. This article examines the relationship between language revitalization backlash and genocide. I argue that language revitalization is part of broader efforts by Indigenous people to reconstitute themselves as distinct groups in reaction to colonial genocides. Backlash against language revitalization can therefore be seen as one element of ongoing efforts to prevent this, leading to a set of social and political relations I call perpetual genocide . I explore the dynamics of language revitalization backlash and perpetual genocide through an analysis of more than 600 social media comments collected from Australia over 2022 and 2023—the opening years of the International Decade of Indigenous Languages—and identify three key themes in these comments: civilizational racism, English and white supremacy, and linguistic diversity as a threat. Based on this analysis, I argue that this backlash, and the perpetual genocide of Indigenous peoples more broadly, is driven by a structural arrangement I call reactionary settler colonialism , which is led by a right-wing vanguard but involves all settlers as implicated subjects. I conclude by discussing counter-genocidal praxis in relation to this formation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".