Methodological Considerations for Survey-Based Research During Emergencies and Public Health Crises: Improving the Quality of Evidence and Communication
Bibliographic record
Abstract
The novel coronavirus (COVID-19) outbreak has resulted in a massive amount of global research on the social and human dimensions of the disease. Between academic researchers, governments, and polling firms, thousands of survey projects have been launched globally, tracking aspects like public opinion, social impacts, and drivers of disease transmission and mitigation. This deluge of research has created numerous potential risks and problems, including methodological concerns, duplication of efforts, and inappropriate selection and application of social science research techniques. Such concerns are more acute when projects are launched under the auspices of quick response, time-pressured conditions–and are magnified when such research is often intended for rapid public and policy-maker consumption, given the massive public importance of the topic.
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 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 | 0.860 | 0.942 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.013 | 0.015 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".