Revisit Language Modeling Competition and Extinction: A Data-Driven Validation
Bibliographic record
Abstract
Understanding language competition and extinction is an interdisciplinary challenge, and math models provide a tool for interpreting linguistic census data and possibly predict the language shift trend at the population scale. In this study, new data from previously examined areas were modeled, specifically Catalan and Spanish in Catalonia, Spanish and English in Houston, Texas, Dutch and French in Brussels, Euskera and Spanish in Spain and French and English in Canada. Three mathematical models of the language competition have been validated. The first is the Abrams-Strogatz model, which treats populations as having two monolingual groups. The second is the Castelló model, which considers bilingual speakers. The third is the Mira model, which considers language competition when the two languages have high similarities. It was found that the some of the data matched Abrams-Strogatz original model, but some divergences could still be addressed. It was also found that the Mira model needs some improvement in how it treats the differences between languages.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".