Political orientation, moral foundations, and COVID-19 social distancing
Bibliographic record
Abstract
During the COVID-19 pandemic, governments have advocated numerous social distancing measures, and compliance with these has likely saved millions of lives globally. In an online sample drawn from the U.S. and Canada (N = 209), participants completed measures of political orientation, moral foundations, and COVID-19 social distancing attitudes and behaviours. A more left-wing political orientation, and greater endorsement of the individualizing moral foundations were significantly related to more positive social distancing attitudes, and greater self-reported compliance with relevant restrictions. A more right-wing political orientation, and greater endorsement of the binding and economic liberty foundations were associated with less positive attitudes and reduced compliance. In a series of mediation analyses, the relationships between political orientation and various social distancing measures were significantly mediated by variations in participants' moral foundations, particularly their endorsement of economic liberty and the individualizing foundations. Further data indicated that the perceived persuasiveness of messages based on each moral foundation advocating for continued social distancing was significantly related to both participants' moral values and their political orientation. Findings are discussed in terms of understanding politicized differences around social distancing as partly reflecting differential valuation of the moral foundations, and in creating effective public health messaging regarding compliance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".