Pandemic politics: policy evaluations of government responses to COVID-19
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
The COVID-19 crisis has demanded that governments take restrictive measures that are abnormal for most representative democracies. This article aims to examine the determinants of the public’s evaluations towards those measures. This article focuses on political trust and partisanship as potential explanatory factors of evaluations of each government’s health and economic measures to address the COVID-19 crisis. To study these relationships between trust, partisanship and evaluation of measures, data from a novel comparative panel survey is utilised, comprising eleven democracies and three waves, conducted in spring 2020. This article provides evidence that differences in evaluations of the public health and economic measures between countries also depend on contextual factors, such as polarisation and the timing of the measures’ introduction by each government. Results show that the public’s approval of the measures depends strongly on their trust in the national leaders, an effect augmented for voters of the opposition. Supplemental data for this article can be accessed online at: https://doi.org/10.1080/01402382.2021.1930754 .
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.
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 | 0.011 |
| 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 it