Types of pandemic-induced psychological distress, clarity of responsibility, and support for incumbents
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
Abstract Will voters punish incumbents for psychological distress associated with public policy during external shocks? This study examines this question in the empirical context of the first wave of the COVID-19 pandemic in India, utilizing three novel cross-sectional surveys conducted in the first three weeks of June 2020, immediately after the national lockdown policy was officially revoked. We find that propensity to vote for the nationally incumbent Bharatiya Janata Party (if hypothetical elections were held on the day of the survey) was negatively correlated with mental stress from routine disruptions in mobility (Week 1); worsening mental health (Week 2); and emotion-focused coping (Week 3). We show that these effects are strongest in BJP-ruled states. We argue that psychological distress shaped political attitudes in the midst of the pandemic and this effect was conditional on the source of distress and moderated by governmental clarity of responsibility.
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 | 0.003 | 0.006 |
| 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