Determining Anti-Curve-Flattening Behaviors for COVID-19 in the United States
Why this work is in the frame
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Bibliographic record
Abstract
COVID-19 has arguably impacted every dimension of social living — be that employ- ment, schooling, healthcare or recreational activities. In a matter of months, businesses have shut down and the workforce and schools have been redirected to online work in many regions of the world. One key element of the North American pandemic response has been the emphasis that the spread or prevention of the pandemic is largely dependent on the measures taken by residents of any region. As such, our research focuses on outlining the factors that determine if an individual is less likely to take this pandemic seriously (i.e. is taking fewer measures to prevent the spread of COVID-19). We have analyzed the results of a U.S. wide COVID-impact survey using random forest classification (RFC) to associate individual demographic factors to measures taken against the pandemic such as washing/sanitizing hands. Our results indicate that the top three influential factors are household size, the number of adults living in one household and the health of the respon- dent (poor to excellent). Using these insights, we used association rules to determine key combinations of features that may lead to an apathetic response to a global pandemic in U.S. citizens, such as lower income households.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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