The gendered dimensions of the anti-mask and anti-lockdown movement on social media
Bibliographic record
Abstract
This paper examines the anti-mask and anti-lockdown online movement in connection to the COVID-19 pandemic. To combat the spread of the coronavirus, health officials around the world urged and/or mandated citizens to wear facemasks and adopt physical distancing measures. These health policies and guidelines have become highly politicized in some parts of the world, often discussed in association with freedom of choice and independence. We downloaded references to the anti-mask and anti-lockdown social media posts using 24 search terms. From a total of 4209 social media posts, the researchers manually filtered the explicit visual and textual content that is related to discussions of different genders. We used multimodal discourse analysis (MDM) which analyzes diverse modes of communicative texts and images and focuses on appeals to emotions and reasoning. Using the MDM approach, we analysed posts taken from Facebook and Instagram from active anti-mask and anti-lockdown users, and we identified three main discourses around the gendered discussion of the anti-mask movement including hypermasculine, sexist and pejorative portrayals of "Karen", and appropriating freedom and feminism discourses. A better understanding of how social media users evoke gendered discourses to spread anti-mask and anti-lockdown messages can help researchers identify differing reactions toward pandemic measures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".