The Political is Personal: The Costs of Daily Politics
Bibliographic record
Abstract
Politics and its controversies have permeated everyday life, but the daily impact of politics on the general public is largely unknown. Here, we apply an affective science framework to understand how the public experiences daily politics. We used longitudinal, daily-diary methods to track two samples of U.S. participants as they experienced daily political events across two weeks (Study 1: N=198, observations=2,167) and three weeks (Study 2: N=811, observations=12,790) to explore how these events permeated people’s lives and how people coped with that influence. In both studies, daily political events consistently evoked negative emotions, which corresponded to worse psychological and physical well-being, but also increased motivation to t¬ake political action (e.g., volunteer, protest) aimed at changing the political system that evoked these emotions in the first place. Understandably, people frequently tried to regulate their politics-induced emotions; and regulating these emotions using effective cognitive strategies (reappraisal and distraction) predicted greater well-being, but also weaker motivation to take action. Although people can protect themselves from the emotional impact of politics, frequently-used regulation strategies appear to come with a trade-off between well-being and action. To examine whether an alternative approach to one’s emotions could avoid this trade-off, we measured emotional acceptance in Study 2 (i.e., accepting one’s emotions without trying to change them), which predicted greater daily well-being but no impairment to political action. Overall, this research highlights how politics can be a chronic stressor in people’s daily lives, underscoring the far-reaching influence politicians have beyond the formal powers endowed unto them.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; a candidate call from one teacher head, 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".