An appeal for practical social justice in the COVID-19 global response in low-income and middle-income countries
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic hit the world's wealthiest countries first, shaping global public health responses and messaging. As the pandemic escalates in low-income and middle-income countries (LMICs), there is a growing call to identify locally tailored solutions.1,2 Because outbreaks are not only public health emergencies, but also political and socioeconomic emergencies, we can learn from African Ebola and cholera responses and avoid “biomedical tunnel vision”3 by actively addressing wider socioeconomic and health inequities.
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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 | 0.032 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.059 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".