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Record W4206364537 · doi:10.1353/dss.2022.0009

Health Nationalism

2022· article· en· W4206364537 on OpenAlexaboutno aff
Stephanie DeGooyer, Srinivas Murthy

Bibliographic record

VenueDissent · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsNationalismSurprisePopulationChinaNothingPolitical scienceEconomic historyHistoryLawSociologyPoliticsDemography

Abstract

fetched live from OpenAlex

Health Nationalism Stephanie DeGooyer (bio) and Srinivas Murthy (bio) On April 9, 2021, only two days after receiving its first shipment of the COVID-19 vaccine, Saint Vincent and the Grenadines suffered an enormous volcanic eruption. Plumes of smoke and ash billowed across the main island, necessitating a large-scale evacuation of its residents to neighboring countries. But, as Prime Minister Ralph Gonsalves explained in an emotional press conference, only vaccinated residents could be evacuated. The transporting ships, and the islands themselves, had vaccine mandates. This meant that only residents with preferential access to the vaccine—;at the time, 10,805 out of a population of approximately 111,000—;had the opportunity to flee. The relationship between health status and freedom of movement is nothing new. A year earlier, in March 2020, as COVID-19 cases skyrocketed around the world, national leaders sought to protect their domestic populations by closing national borders. "Heath nativism," we wrote in this magazine, was more than just public-health policy; it was an illogical attempt "to keep out that which is already inside, put borders around a problem that is borderless, and shore up an illusion of safety by projecting the origin of the problem as always somewhere else." It was no surprise that Donald Trump would respond to a major health crisis by promoting a border wall and attacking China on Twitter. Trump, like many leaders before him, was leaning on a nationalistic association of disease and the enemy. COVID-19 is "our big war," he declared in March 2020. "It's a medical war. We have to win this war." At the time, we cautiously hoped a global response to the pandemic might still materialize. Scientists were only beginning to learn about the virus. There was still time for world leaders to form a collective plan. In the ensuing months, there was a Herculean effort to develop effective vaccines. But, despite the successful delivery of several vaccines, health nativism has only grown more entrenched. The problem is not just right-wing xenophobia but the purportedly science-based general response of the world's richest nations to a major global threat. Policies regulating vaccine access and [End Page 69] Click for larger view View full resolution Pfizer's COVID-19 vaccine pictured on December 15, 2020 (Ariana Drehsler/AFP via Getty Images) [End Page 70] border closures have been dressed up as public-health initiatives but, in reality, have tended to support the same nationalistic agendas. The international cooperation needed to end this pandemic—;and prepare for future ones—;appears as far off as ever. When Joe Biden became president, one of his first actions was to rescind Trump's travel ban against travelers from primarily Muslim and African countries. On his second day in office, Biden signed an executive order saying the ban had "jeopardized our global network of alliances and partnerships" and "dulled the power of our example the world over." Apparently the same was not true of Trump's other travel bans. Under Biden, the country's borders with Canada and Mexico remained closed to non-essential travel. The administration also extended Trump's ban on travelers from the United Kingdom, Ireland, Brazil, and the twenty-six countries in the Schengen border-free area of Europe. South Africa was added to the list of restricted countries. At the end of April, citing concerns about the spread of the Delta variant, Biden imposed restrictions on travelers from India. Biden has used scientific language to distinguish his reasons for imposing travel restrictions from the racism and xenophobia of his predecessor. But border shutdowns rarely work to stop the progression of disease. Travel bans could, in theory, keep out the coronavirus, if they were near total and rigorously enforced. This is not what has happened. Instead, border policies have been implemented in a haphazard manner based on market imperatives rather than public-health policy. A large proportion of the initial spread of COVID-19, in early 2020, was due to people scrambling to airports before selective border closures went into effect. Since then, domestic case numbers have soared in rich nations, even as governments have continued to ban travel from countries deemed higher...

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.445

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0070.004
Open science0.0010.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.1330.024

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.

Opus teacher head0.032
GPT teacher head0.318
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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