“Besides the Bread, Everyone Speaks Tibetan”: A Portrait of the Tibetan Occupational Link to Farmers Markets in New York City
Bibliographic record
Abstract
This paper explores farmers markets as significant sites of employment, language use, and cultural expression for Tibetan speakers living in New York City. Farmers markets serve as a labor niche for the large Tibetan speaking migrant community in the city, yet this niche has been largely unnamed in discussions of Himalayan New York and in discussions of immigrant labor patterns in general. Guided by ethnographic research and first-hand interviews, this paper seeks to investigate the occupational link between farmers markets and Tibetan speakers in NYC – how this niche developed and is sustained, how employee values and the employment environment inform and aid this connection, and how Tibetan language and tradition manifest within the farmers market sphere. Ultimately, this paper conceptualizes farmers markets as spaces of community beyond labor. A seemingly simple employment pattern is in fact an extensive microcosm of Tibetan community spanning five boroughs and over fifty farmers markets, aiding linguistic and cultural preservation as well as exchange for Tibetan speakers across 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".