Impact of face masks on perceptions of black and white targets during the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract Although the use of face masks was widespread during the COVID‐19 pandemic, their impact on social perceptions is unclear. Notably, research that has investigated the influence of masks on personality attributions has focused on a small set of characteristics with a focus on predominantly White targets, and only few studies examining more diverse groups. Because the race of targets has been found to impact impression formation processes in significant ways, it is important to examine diverse racial targets along with a wider range of personality traits. The goal of the present research therefore was to explore how face masks impact a variety of trait attributions for both White and Black targets. Our results indicate that masking faces has positive implications (i.e., more trustworthy, warm, competent, and less threatening) for White but not Black targets. Notably, both White and Black targets with masks compared to without masks were perceived as more attractive, but the effect was smaller for Black targets. Because COVID‐19 continues to be a public health emergency of international concern, with infections and deaths occurring around the world and with mask mandates still being implemented in a variety of contexts, knowing how people differentially respond to targets of different races wearing masks remains relevant and important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".