The ethics of indigenous language revitalization
Bibliographic record
Abstract
Abstract The ethical issue we address in this chapter is the role of Indigenous language experts who do not live in the community where an Indigenous language is spoken. Specifically, we question the ethics as well as the ethical protocols for engaging in research with Indigenous language speakers in the context of language revitalization discourses. We suggest that any judgments or decisions made by non-Indigenous language speakers with regard to standardization, orthography, digitization, pedagogy, and advocacy must be regarded as attempts at cultural and linguistic appropriation. We suggest that archiving or documenting Indigenous languages is best considered linguistic taxidermy, another move of colonization that we call fina-colonialism. In short, with reference to the specific languages of Tokunoshima, Japan, we discuss the ethics of research that purportedly aims at decolonizing, but in which Indigenous language speakers are rendered exotic representations of their own identities, commodified according to cosmopolitan interests and global tastes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".