Book Review of <i>Native American Language Ideologies: Beliefs, Practices,and Struggles in Indian Country</i> edited by PaulV. Kroskrity and Margaret C. Field
Bibliographic record
Abstract
As its editors note, this collection is the first work on language ideology especially devoted to Native American languages. Its twelve articles (plus the editors’ introduction) mainly involve languages of the United States (with one each from Canada and Central America) and represent a mix of contributions by Native and non-Native scholars. The offerings generally center on the authors’ own field research, often supplemented by historical and linguistic background from secondary sources. Several themes run through many of these studies. One is a rejection of the notion that a language ideology is the monolithic stance of an entire culture. There is ample demonstration of the heterogeneity of ideologies in relation to socially defined categories (and indeed, individuals). Another theme is reflexivity, as exemplified, for example, by the effect that the recent academic valorization of Indigenous languages has had on the ideologies of some tribes (in the paper by Gómez de García, Axelrod, and Lachler). In addition, the relationship between language ideologies and language maintenance and revitalization is explored in a number of contexts. Other issues include literacy and writing systems, standardization, and ideologies relating to the dominant culture language, to name just a few.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".