COVID-19 & Anti-Mask Movement: How Jingoism is Bringing the United States Down
Bibliographic record
Abstract
The objective of this study was to provide insight into the anti-mask phenomenon that has been occurring throughout the world. Widely broadcasted through different forms of media, these anti-mask movements are a growing concern to the scientific community, as such exposure will only deter the progress towards ending the pandemic. In order to understand the psychological motivations behind the anti-mask sentiment, the present studies 29 videos, over 120 minutes of content covering anti-mask protests in Canada, Europe, and the United States. I also used East Asia as a control variable, as I reviewed 5 videos, around 35 minutes of footage to understand the psychology that makes East Asia more receptive towards mask use. By implementing a qualitative research design, I looked for key language themes (interviews, chants, signs) in order to apply thematic analysis to connect their negative sentiments that are associated with confirmation bias and motivated reasoning. Findings regarding confirmation bias and motivated reasoning have been linked to concerns regarding personal rights and distrust with the government, media, and science communities. In particular, the United States has an issue regarding national pride in connection to individuals’ personal rights. The goal is to give insight into ways the United States can improve mask adherence for future potential pandemics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".