Boredom proneness, political orientation and adherence to social-distancing in the pandemic.
Bibliographic record
Abstract
Research recently showed that boredom proneness was associated with increased social distancing rule-breaking in a sample collected early in the COVID-19 pandemic. Here we explore data collected early in the pandemic to examine what factors might drive this relation. We focus on political affiliation. Given the functional account of boredom as a call to action, we hypothesized that this urge to act may drive individuals towards outlets replete with symbolic value (e.g., ideology, identity). In addition, given the politicization of some social distancing rules (e.g., mask wearing), we explored whether those who adhere to strong political ideologies—particularly conservative ideologies—would be more likely to rule-break. Moderation analyses indicated that boredom proneness and social (but not fiscal) conservatism were indeed predictive of rule-breaking. These results highlight the need for both clear messaging emphasizing the strength of communal identity and action (i.e., that “We are all in this together”) and for interventions that emphasize shared collective values in contexts that appeal directly to social conservatives.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".