A Scoping Review of Published Literature on the Linguistic Representation of Indigenous Peoples
Bibliographic record
Abstract
Published research involving Indigenous Peoples is largely deficit-based, which can perpetuate stereotypes against Indigenous Peoples. We conducted a scoping review to understand what is currently known about the linguistic representation of Indigenous Peoples. We included peer-reviewed articles from all disciplines published between 2000 and 2024 on language use and discourse in the context of framing, bias, and/or stereotyping of Indigenous Peoples in Canada, the United States, Australia, and Aotearoa New Zealand. Of 1672 articles, 80 were reviewed and analyzed by mode of language, field of study, and time. A subset of the articles (n = 60) underwent a reflexive thematic analysis, from which we identified seven themes. We found that linguistic representations of Indigenous Peoples were disproportionately negative and involved deficit- rather than strengths-based discourse. Greater attention to linguistic representations of Indigenous Peoples is needed within healthcare and education, and future research should include language in historical documents and academia.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".