Identities of Resignation: Threats to Indigenous Languages from Neoliberal Linguistic and Educational Practices
Bibliographic record
Abstract
We suggest that while Indigenous languages are threatened by capitalist and neoliberal encroachments, responses from applied linguists in the academy can be misguided. To make our argument, we must first define neoliberalism, and examine how the broader neoliberal discourses of choice, competition and the free market have percolated and distilled into local Indigenous language contexts, impacting languages, cultures and identities. We ask ourselves what identities are currently available, adopted and valorized by and for Indigenous language speakers, and how positions like Indigenous language speaker, academic/linguist, activist and teacher are altering in response to available neoliberal subject positions? We suggest that neoliberal discursive regimes position Indigenous peoples who do not speak their heritage languages as “victims needing recognition and redress.” The result is that they have become trapped in colonizer ideologies viewing Indigenous peoples as unfit to govern themselves. Colonized now by neoliberalism, Indigenous language speakers forced to live within neoliberal regimes must adopt identities of resignation, meaning engaged in a permanent struggle to accommodate themselves to the world. Instead, we posit that their positions are better framed and respected as identities of refusal, everyday actions of refusing enclosure.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.053 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".