Between rules and resistance: moving public health emergency responses beyond fear, racism and greed
Bibliographic record
Abstract
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.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.069 |
| Scholarly communication | 0.040 | 0.041 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.021 | 0.029 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".