Symbolic show of strength: a predictor of risk perception and belief in misinformation
Bibliographic record
Abstract
To some, measures to curb COVID-19 were reasonable and prudent; to others, they were unacceptable signs of losing a more symbolic battle. We propose that such symbolic thinking is key to how people perceive reality. We report three studies (total N = 5535 across eight countries, conducted during the COVID-19 pandemic) linking what we term Symbolic Show of Strength (SSS) in the context of COVID (SSS-COVID) with several important outcomes. Across countries, SSS-COVID was the strongest predictor of perception of COVID-19’s danger, attitudes toward vaccines, and belief in COVID-related misinformation in multiple regressions taking into a host of other reasoning and sociopolitical variables. In a fourth study (N = 430) we adapt the concept to attitudes toward cryptocurrency, with SSS-Crypto uniquely predicting perceived risk of cryptocurrency, general conspiracy beliefs, and preferences for autocratic government. Our results also suggest that SSS shapes perceptions of products, marketing ethics, and symbols more broadly.
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 | 0.002 | 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 | 0.000 | 0.000 |
| 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; 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".