Mapping Urban Linguistic Diversity in New York City: Motives, Methods, Tools, and Outcomes
Bibliographic record
Abstract
Communities around the world have distinctive ways of representing language use across space and territory. The approach to and method of mapping languages that began with nineteenth-century European dialectology and colonial boundary making is one such way. Though practiced by relatively few linguists today, language mapping has developed considerably from its roots yet remains stymied by problems of ideology, representation, and data quality. In this paper, we argue that digital language mapping in hyperdiverse cities can both contribute to overcoming these problems and bring visibility and resources to communities using Indigenous, minority, and primarily oral languages. For these communities, official surveys like the census are often inadequate, leaving a gap that communities, linguists, and mapping experts working in partnership can address. Urban language mapping as a field should make space for Indigenous, minority, and primarily oral languages through geospatial visualization – in terms that the communities themselves recognize and with a public policy agenda. As a case study, we present our ongoing efforts with LANGUAGEMAP.NYC to map the most linguistically diverse urban center in the world: New York City.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".