Pandemic, but Make It Fashion: Ukrainian Embroidered PPE in the Time of COVID-19
Bibliographic record
Abstract
Embroidered pandemic wear has become one of the newest cultural fashion trends to emerge in Ukraine and within its Canadian diaspora. This article explores the ways in which embroidery as a traditional form of culture retains meaning within modern contexts, while also serving as a vehicle for experimenting with atypical applications of cultural symbols and representations. Throughout the COVID-19 pandemic, cloth masks have been recommended by public health officials, including the World Health Organization, as a preventative measure to limit the spread of the virus. On the basis of digital fieldwork, I discuss the meanings and inspirations behind these embroidered masks, while conducting a material culture analysis of the objects themselves. I argue that, through a subversion of their common purpose— to hide one’s identity— masks have been used in the pandemic as an open/performative display of culture. I contend that this display acts as a means to promote tradition through ephemera and assert cultural importance. This, coupled with the personal/private use of embroidery as a protective talisman, has fueled a trend of embroidered personal protective equipment in popular culture. In this article, I examine the purpose, use, and form of these masks in order to bring light to the ways in which cultural traditions and objects act (and developed prevalence) as a form of pandemic response.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".