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Record W4311550352 · doi:10.1136/bmjgh-2022-009945

Between rules and resistance: moving public health emergency responses beyond fear, racism and greed

2022· review· en· W4311550352 on OpenAlexaff
Carly Jackson, Roojin Habibi, Lisa Forman, Diego S. Silva, Maxwell J. Smith

Bibliographic record

VenueBMJ Global Health · 2022
Typereview
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsWestern UniversityYork UniversityPublic Health OntarioUniversity of Toronto
FundersWorld Health Organization
KeywordsRacismResistance (ecology)Public healthCriminologyPolitical sciencePsychologySociologySocial psychologyMedicineNursingLaw

Abstract

fetched live from OpenAlex

In times of a public health emergency, lawyers and ethicists play a key role in ensuring that government responses, such as travel restrictions, are both legally and ethically justified. However, when travel bans were imposed in a broadly discriminatory manner against southern African countries in response to the Omicron SARS-CoV-2 variant in late 2021, considerations of law, ethics or science did not appear to guide politicians' decisions. Rather, these bans appeared to be driven by fear of contagion and electoral blowback, economic motivations and inherently racist assumptions about low-income and middle-income countries (LMICs). With a new pandemic treaty and amendments to the WHO's International Health Regulations (IHR) on the near-term horizon, ethics and international law are at a key inflection point in global health governance. Drawing on examples of bordering practices to contain contagion in the current pandemic and in the distant past, we argue that the current IHR is not adequately constructed for a just and equitable international response to pandemics. Countries impose travel restrictions irrespective of their need or of the health and economic impact of such measures on LMICs. While the strengthening and reform of international laws and norms are worthy pursuits, we remain apprehensive about the transformative potential of such initiatives in the absence of collective political will, and suggest that in the interim, LMICs are justified in seeking strategic opportunities to play the same stark self-interested hardball as powerful states.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.307
GPT teacher head0.564
Teacher spread0.258 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2022
Admission routes1
Has abstractyes

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