How Do Politicians Speak About Economic Policy During Pandemic Crisis? Evidence From Emerging and Developed Countries
Bibliographic record
Abstract
Our study explores economic policy communication in response to the Covid-19 pandemic crisis. Considering a major role of Twitter in information dissemination, we use tweets as a proxy to examine politicians’ crisis communication strategies in five countries, Australia, Canada, India, Indonesia, and Singapore. By using systematic content analysis approach, the study attested the degree to which SCCT and IRT model can be applied to political realm. We found two strategies, bolstering and mortification, emerge as the most frequently used strategies by politicians. Further, new strategies, i.e information provision and cohesion, as well as new categories, i.e morale boosting, political positioning, and cross border cooperation surfaced which further expanding the SCCT and IRT model in explaining political crisis communication. As this study explores the role of context and situational factors that determine specific strategies, our findings demonstrate no substantial differences among developed and emerging countries. We note the use of combination of bolstering, mortification, and cohesion strategies can be critical for politicians’ career, as they may restore politicians’ reputation, reinforce their political presentation, and foster public trust.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".