Pandemic-Driven Leadership Perceptions: Attribution Theory in the 2020 U.S. Presidential Election
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted critical aspects of leadership and public trust amidst a health crisis of unprecedented scale in the United States, especially within a context of significant political polarization. This research paper explores the nuances of leadership effectiveness during the pandemic, focusing on the role of attribution theory in understanding public perceptions of leadership actions. The theory elucidates how the public interprets leaders’ responses to the pandemic, influenced by factors such as political affiliations, societal norms, and racial considerations. The paper investigates the interplay between incumbent advantage theory and the rally ’round the flag effect, alongside the impact of President Trump’s handling of the pandemic on public opinion. It delves into the complexities introduced by the pandemic’s racial dimensions and its effects on minority communities, examining the broader implications for leadership responsiveness and public trust. Using a comprehensive analysis of demographic and psychographic variables, the study reveals a significant negative outlook on Trump’s crisis management across various groups. The findings underscore a complex interplay of media influence, racial identity, health status, financial conditions, and political affiliations in shaping perceptions of leadership accountability. Through the lens of attribution theory, the paper offers a nuanced understanding of the psychological and political dynamics that affect public attitudes toward leadership and accountability during crises.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".