Opening Plenary From Yul, Shöyul, and Phayul to the Vertical Village: Documenting and Developing Languages in the Urban Diaspora
Bibliographic record
Abstract
Contemporary New York City—part of Lenapehoking, the land of the Lenape people—is now a last improbable refuge for hundreds of minoritized, Indigenous, and primarily oral languages from all over the world. Never before have cities been so linguistically various, and they may never be again given mounting threats, but so far the new urban linguistic diversity has hardly been recognized, understood, or supported. The non-profit Endangered Language Alliance (ELA) was founded in 2010 with a mission to document endangered languages and support linguistic diversity in New York and beyond. Over the last 15 years, as described in ELA co-director Ross Perlin’s new book Language City: The FIght to Preserve Endangered Mother Tongues in New York, ELA has forged a new form of public linguistics, bringing together linguists, language activists, students, and ordinary New Yorkers to collaborate in long-term partnership on a range of language projects. Among them is the documentation and description of Seke, a Trans-Himalayan (Tamangic) language with approximately 700 speakers from five villages of northern Nepal, over a quarter of whom have now come to New York and are clustered in a few of the city’s vertical villages. Recording elders across the Seke world and creating core materials for the language have been priorities for Rasmina Gurung, one of the youngest speakers, while also acknowledging and grappling with multilingualism, mixing, translocalism, mobility, and rapid cultural shift — all central concerns of the new urban linguistics. (Presented in partnership with Daniel Kaufman and Bikas Gurung.)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".