Wayfaring in Taiwan during COVID-19: Reflections on Political Ontologies of Disease and Geopolitics
Bibliographic record
Abstract
What are the political and ontological implications of COVID‑19? I had plenty of time to reflect on this from March to July after I ended fieldwork in Guam and unexpectedly spent four months in Taiwan. Because of Taiwan’s proximity to China, where the pandemic began, it initially seemed as if it would be among the most serious cases. Instead, Taiwan’s public health measures allowed it to become one of the few places in the world relatively untouched by the virus. The experience of Taiwan with COVID‑19 was shaped most of all by tense relations with China and the non-recognition of the country by the World Health Organization (WHO). There are also intriguing differences within Taiwan where historically Chinese settler groups and Indigenous peoples related to other Pacific Islanders find their place in the world through a broad spectrum of non-Western ontologies. In travelogue genre, I reflect upon their different stories and practices of worlding as fears of the pandemic ontributed to a heightened sense of crisis, ethnic tensions, and a rise in nationalism. This reveals important ontological differences that will continue to influence the geopolitics of the region even beyond the current pandemic.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| 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".