Dynamics of behavior change in the <scp>COVID</scp> world
Bibliographic record
Abstract
All of the policies adopted or proposed so far to slow the spread of the novel coronavirus require immediate and extensive behavioral change. However, even when the benefit of the behavior change is supported by solid science, actually changing behavior is difficult. Doing so effectively requires an appreciation for how people learn behaviors, and translate information into action. Insights from the evolutionary human sciences can improve the behavioral change toolkit for researchers and policy makers. Specifically, effective behavior change policy should be based on an understanding of humans as a cultural and cooperative species. Socially transmitted information and culturally-informed motivations shape behavior change. The structure of social networks and how group identities map onto those networks in turn influence transmission dynamics. Information can spread from person to person, similar to the way diseases spread. Just as with disease, the epidemiology of information is subject to structural and behavioral influences on transmissibility. We show why and how 1) the pandemic poses several adaptive challenges with important tradeoffs, 2) people use social information to learn how to deal with these, and 3) people adopt social norms in a group-based context.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".