Containing COVID-19 by Matching Messages on Social Distancing to Emergent Mindsets—The Case of North America
Bibliographic record
Abstract
Public compliance with social distancing is key to containing COVID-19, yet there is a lack of knowledge on which communication 'messages' drive compliance. Respondents (224 Canadians and Americans) rated combinations of messages about compliance, systematically varied by an experimental design. Independent variables were perceived risk; the agent communicating the policy; specific social distancing practices; and methods to enforce compliance. Response patterns to each message suggest three mindset segments in each country reflecting how a person thinks. Two mindsets, the same in Canada and the US, were 'tell me exactly what to do,' and 'pandemic onlookers.' The third was 'bow to authority' in Canada, and 'tell me how' in the US. Each mindset showed different messages strongly driving compliance. To effectively use messaging about compliance, policy makers may assign any person or group in the population to the appropriate mindset segment by using a Personal Viewpoint Identifier that we developed.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".