‘The rez accent knows no borders’: Native American ethnic identity expressed through English prosody
Bibliographic record
Abstract
Abstract In many Native American and Canadian First Nations communities, indigenous languages are important for the linguistic construction of ethnic identity. But because many younger speakers have limited access to their heritage languages, English may have an even more important role in identity construction than Native languages do. Prior literature shows distinctive local English features in particular tribes. Our study builds on this knowledge but takes a wider perspective: We hypothesize that certain features are shared across much larger distances, particularly prosody. Native cultural insiders (the first two co-authors) had a central role in this project. Our recordings of seventy-five speakers in three deliberately diverse locations (Standing Rock Sioux Reservation, North/South Dakota; Northwest Territories, Canada; and diverse tribes represented at Dartmouth College) show that speakers are heteroglossically performing prosodic features to index Native ethnic identity. They have taken a ‘foreign’ language (English) and enregistered these prosodic features, creatively producing and reproducing a shared ethnic identity across great distances. (Native Americans, prosody, ethnicity, ethnic identity, English, dialects)*
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".