Global Pandemic, Translocal Medicine
Bibliographic record
Abstract
Abstract This article analyzes the audio diaries of a Tibetan physician, originally from Amdo (Qinghai Province, China), now living in New York City. Dr. Kunchog Tseten describes his experiences during the first wave of the COVID-19 pandemic, in spring and summer 2020, when Queens, New York—the location where he lives and works—was the “epicenter of the epicenter” of the novel coronavirus outbreak in the United States. The collaborative research project of which this diary is a part combines innovative methodological approaches to qualitative, ethnographic study during this era of social distancing with an attunement to the relationship between language, culture, and health care. Dr. Kunchog’s diary and our analysis of its contents illustrate the ways that Tibetan medicine and Tibetan cultural practices, including those emergent from Buddhism, have helped members of the Himalayan and Tibetan communities in New York City navigate this unprecedented moment with care and compassion.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".