Looking for Safety in All the Right Places: When Threatening Political Reality Strengthens Family Relationship Bonds
Bibliographic record
Abstract
Elections and pandemics highlight how much one’s safety depends on fellow community members, a realization that is especially threatening when this collective perceives political realities inconsistent with one’s own. Two longitudinal studies examined how people restored safety to social bonds when everyday experience suggested that fellow community members inhabited inconsistent realities. We operationalized consensus political realities through the negativity of daily nationwide social media posts mentioning President Trump (Studies 1 and 2), and the risks of depending on fellow community members through the pending transition to a divided Congress during the 2018 election season (Study 1), and escalating daily U.S. COVID-19 infections (Study 2). On days that revealed people could not count on fellow community members to perceive the same reality of President Trump’s stewardship they perceived, being at greater risk from the judgment and behavior of the collective community motivated people to find greater happiness in their family relationships.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".