Testing Negative: The Non-Consequences of COVID-19 on Mass Ideology
Bibliographic record
Abstract
Responding to COVID-19, governments implemented large-scale economic and social policies of unprecedented scale. This highlighted the state's capacity to guarantee economic and health security, and affected demographic groups that are less commonly beneficiaries of state support. We hypothesise that exposure to the pandemic and these policy responses caused change in attitudes to the role of government in the economy and redistribution. We test this expectation using data from the (2014–present) British Election Study panel, together with a unique panel survey fielded to existing BES respondents in April and September, 2020. We find virtually no evidence of any effect on ideological beliefs. Moreover, using a survey experiment, we find exposure to cues linking the pandemic to greater roles for government has no impact on ideological beliefs. We conclude that such elite rhetoric, even if it had been present in the field, would not have yielded ideological change.
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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 | 0.001 | 0.010 |
| 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 | 0.003 |
| 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".