Unsettling the American Dream: Mobility, Migration and Precarity among Translocal Himalayan Communities during COVID‐19
Bibliographic record
Abstract
New York City (NYC) garnered significant national and international attention when it emerged as the coronavirus epicentre in the USA, in spring 2020. As has been widely documented, this crisis has disproportionately impacted minority, immigrant and marginalized communities. Among those affected were people from Mustang, Nepal, a Himalayan region bordering Tibet. This community is often rendered invisible within larger Asian immigrant populations, but the presence of Mustangis in the US has transformed their translocal worlds, lived between Nepal and NYC. Seasonal mobility and life-stage wage labour in cosmopolitan Asia have been common in Mustang for decades. More permanent moves to NYC began in the 1990s. These migrations were based on assumptions about attaining financial stability in the US in ways deemed unattainable in Nepal. An ethnographic focus on one translocal Mustangi family frames this discussion of how COVID-19 has overturned previously held ideas around migration to NYC and uncovered new forms of precarity. The authors build on theories of transnationalism and translocality to position migration as a cyclical process whereby the well-being of Mustangis in Nepal and NYC rests on the reliability of global migratory networks and translocal kinship relations - a basis for security and belonging that COVID-19 has challenged and reconfigured.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".