Belief in Science and Attitudes Toward COVID-19: A Demographic Standardization Approach to China–US Comparison, 2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS TOPIC?: Attitudes of disapproval toward public health measures led to behaviors that could increase vulnerability to contracting coronavirus disease 2019 (COVID-19). WHAT IS ADDED BY THIS REPORT?: . 4.03 on a 1-5 scale). The China-US difference was attributable to 1) Chinese citizens having more confidence in scientists than Americans and 2) Chinese citizens almost invariably accepting the necessity of COVID-19 mitigation measures, regardless of their confidence in scientists. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE?: Building public support for population health measures and public trust in science is crucial for handling epidemic crises.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it