A Virus That Knew Borders. COVID-19 Patients Zero Worldwide and the Strength of Transnationalism
Bibliographic record
Abstract
In less than 3 months in 2020, COVID-19 spread to more than 200 countries and turned into a global pandemic that affected all world regions. Far from being a “post-Westphalian” virus that knew no borders, COVID-19 remained embedded in unequal patterns of international mobilities. To substantiate this claim, I devised an original methodology inspired by “thing-following studies”. A dataset was created on all patients zero worldwide (n = 287) in the 206 countries where they were identified. Empirically, my findings dismantle some myth about the international spread of COVID-19. First, the data put Europe – rather than China – at the core of the pandemic: four Western European countries exported half of all patients zero to entire regions like South America or Africa, reflecting postcolonial legacies. Second, twothirds were in fact nationals who brought back the virus to their own country. Third, a majority were involved in cross-border activities relating to business, family, religion or education, rather than tourism – most of them middle-aged men. Theoretically, this demonstrates the strength of transnational activities among international mobilities. Transnationalism appears as a crucial – though deeply unequal – infrastructure of our current globalization.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".