Voters’ view of leaders during the Covid‐19 crisis: Quantitative analysis of keyword descriptions provides strength and direction of evaluations
Bibliographic record
Abstract
Objectives: Previous research suggests that governments usually gain support during crises such as the Covid-19. However, these findings are based on rating scales that only allow us to measure the strength of this support. This article proposes a new measure of how voters evaluate Prime Ministers (PM) by asking for descriptive keywords that are analyzed by natural language processing. Methods: By collecting a representative sample of citizens' own key words describing their PM in 15 countries in Europe during the outbreak of Covid-19, and analyzing these by latent semantic analysis and a multiple OLS regression, we could quantify the strength and direction of voters' view. Results: The strength analysis supported previous studies that describing the PM with positive words was strongly associated with vote intention. Furthermore, a change in the direction of the attitudes from "good" to "honest" was found. A new finding was that the pandemic was associated with an increase in polarization. Conclusions: The keyword evaluation analysis provides opportunities of evaluating both strength and direction of voters' view of their PM, where we show new results related to increased polarization and shift in the direction of attitudes.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".