Bibliographic record
Abstract
Despite sharing many cultural, historical, and socioeconomic characteristics, Totonac communities have markedly distinct language use patterns and practices. Some communities have adopted the mainstream hegemonic discourse in Mexico that denigrates indigeneity and subsequently abandoned Totonac (Lam 2009). In other communities, such as Ozelonacaxtla, an alternate discourse dominates that values multilingualism, and Totonac is widely spoken by the vast majority of the community. This variation across Totonac communities facing the same broad pressures to shift to Spanish demonstrates that current sociodemographic models of language shift lack significant predictive power. By examining not only sociodemographic factors, but also language ideology, this study seeks to determine whether and how language use in Ozeloancaxtla is qualitatively different in nature from other Totonac communities. Interpreting language use in Ozelonacaxtla is undertaken in the methodology of qualitative linguistic ethnography (Copland & Creese 2015). Results show that Ozelonacaxtla Totonac is currently used in almost all community and home domains; however some threats to continued sustainability are recognized. Three main language ideologies in Ozelonacaxtla are identified: (i) language is an index of identity, (ii) language is important/useful, and (iii) Totonac should not be lost. These main discourses are used by speakers to explain, justify, and contest language use patterns and practices, and significant differences in ideology are found across Totonac communities with contrasting language use. This demonstrates the importance of examining ideology in order to accurately interpret language use and best position potential efforts to support language sustainability, documentation, and revitalization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".