Politics of prevention in the periphery: The initial response to COVID-19 on Barbuda and Puerto Rico
Bibliographic record
Abstract
The islands of Barbuda and Puerto Rico share a history of dispossession and exploitation, occupying a peripheric position in a core–periphery world system. Yet, each island's response to COVID-19, and the subsequent effects of the pandemic, could not be more different. This paper examines how colonialism and neocolonialism affected the islands’ ability to respond to COVID-19. Barbuda relied on community traditions of support and self-reliance and was able to restrict all travel to and from the island, including travelers from the diaspora and those participating in its informal economic sector. In doing so, Barbuda effectively isolated itself from infection. On the other hand, Puerto Rico, in a protracted economic crisis, was particularly vulnerable to touristic flows, diasporic movements, and a large informal sector. The Puerto Rican response was shaped by deep politicization in the mainland U.S., which complicated an evidencebased strategy to combat the emergency. These cases show that islands, particularly those located in peripheric or subaltern spaces, cannot isolate themselves from the worst effects of COVID-19 through mere geography. Pandemics are not only driven by biological events but also by the narratives of colonialism, encompassing political, economic, and cultural factors, which determine their trajectories — sometimes with devastating outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".