Lockdowns and low- and middle-income countries: building a feasible, effective, and ethical COVID-19 response strategy
Bibliographic record
Abstract
Lockdowns can be an effective pandemic response strategy that can buy much needed time to slow disease transmission and adequately scale up preventative, diagnostic, and treatment capacities. However, the broad restrictive measures typically associated with lockdowns, though effective, also comes at a cost - imposing significant social and economic burdens on individuals and societies, especially for those in low- and middle-income countries (LMICs). Like most high-income countries (HICs), many LMICs initially adopted broad lockdown strategies for COVID-19 in the first wave of the pandemic. While many HICs experiencing subsequent waves have returned to employing lockdown strategies until they can receive the first shipments of COVID-19 vaccine, many LMICs will likely have to wait much longer to get comparable access for their own citizens. In leaving LMICs vulnerable to subsequent waves for a longer period of time without vaccines, there is a risk LMICs will be tempted to re-impose lockdown measures in the meantime. In response to the urgent need for more policy development around the contextual challenges involved in employing such measures, we propose some strategies LMICs could adopt for safe and responsible lockdown entrance/exit or to avoid re-imposing coercive restrictive lockdown measures altogether.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".