Lifting lockdown policies: A critical moment for COVID-19 stigma
Bibliographic record
Abstract
COVID-19 'lockdown' policies may have unintended consequences for individuals, households and country economies. Hence lockdown may be unsustainable despite the risk of a resurgence of new COVID-19 infections. The repeal and alteration of lockdown policies mark a symbolic transfer of responsibility for epidemic control from state to individual. This has the potential to catalyse fear, blame and judgement within and between populations. We draw on experience from the HIV pandemic to show that this will worsen during later phases of the pandemic if COVID-19 stigma increases, as we fear it could. We suggest policy recommendations for 'lockdown lifting' to limit COVID-19 stigma. We suggest three policy priorities to minimise potential increases in COVID-19 stigma: limit fear by strengthening risk communication, engage communities to reduce the emergence of blaming, and emphasise social justice to reduce judgement. 'Lockdown' policies cannot continue uninterrupted. However, lifting lockdown without unintended consequences may prove harder than establishing it. This period has the potential to see the emergence of fear, blame and judgement, intersecting with existing inequalities, as governments seek to share responsibility for preventing further Sars-Cov-2 transmission. As we have learned from HIV, it is critical that a wave of COVID-19 stigma is prevented from flourishing.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".