An Outbreak of Selective Attribution: Partisanship and Blame in the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Crises and disasters give voters an opportunity to observe the incumbent’s response and reward or punish them for successes and failures. Yet, even when voters perceive events similarly, they tend to attribute responsibility selectively, disproportionately crediting their party for positive developments and blaming opponents for negative developments. We examine selective attribution during the COVID-19 pandemic in the United States, reporting three key findings. First, selective attribution rapidly emerged during the first weeks of the pandemic, a time in which Democrats and Republicans were otherwise updating their perceptions and behavior in parallel. Second, selective attribution is caused by individual-level changes in perceptions of the pandemic. Third, existing research has been too quick to explain selective attribution in terms of partisan-motivated reasoning. We find stronger evidence for an explanation rooted in beliefs about presidential competence. This recasts selective attribution’s implications for democratic accountability.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 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 it