Exploring the role of uncertainty, emotions, and scientific discourse during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract This article examines the interplay between uncertainty, emotions, and scientific discourse in shaping COVID-19 policies in Quebec, Canada. Through the application of natural language processing (NLP) techniques, indices were developped to measure sentiments of uncertainty among policymakers, their negative sentiments, and the prevalence of scientific statements. The study reveals that while sentiments of uncertainty led to the adoption of stringent policies, scientific statements and the evidence they conveyed were associated with a relaxation of such policies, as they offered reassurance and mitigated negative sentiments. Furthermore, the findings suggest that scientific statements encouraged stricter policies only in contexts of high uncertainty. This research contributes to the theoretical understanding of the interplay between emotional and cognitive dynamics in health crisis policymaking. It emphasizes the need for a nuanced understanding of how science may be used in the face of uncertainty, especially when democratic processes are set aside. Methodologically, it demonstrates the potential of NLP in policy analysis.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".