Were Americans’ Political Attitudes Linked to Objective Threats From COVID-19? An Examination of Data From Project Implicit During Initial Months of the Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has created objectively threatening situations in everyday life (e.g., unemployment, risk of infection), and researchers have begun to ask whether threats from the pandemic are linked to people’s political attitudes. However, scholars currently lack a systematic answer to this question. Here, we examined whether objective COVID-19 threats (cases, deaths, and government restrictions) occurring over the initial months of the pandemic (February–June 2020) were associated with seven different assessments of political attitudes among Project Implicit users in the United States ( N = 34,581). We did not consistently observe meaningful associations between COVID-19 threats and political attitudes. The lack of consistent meaningful associations emerged regardless of the level of analysis (country, state, and county) or participant’s self-identified ideology. Collectively, these findings failed to find evidence that political attitudes were tied to COVID-19 threats in a meaningful way during the initial months of the pandemic.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".