Performing in the pandemic: The COVID-19 chronicles of Asian mother artists
Bibliographic record
Abstract
This article seeks to collect stories and make heard the voices of mother artists of Asian descent/Asia, about their experiences during the COVID-19 pandemic. The focus is on documenting the implications of their experiences during the pandemic, on their maternal role performance and their studio practice. Some of the points of departure proposed to the artists include disruptions in routines, re-prioritization of work, changes in durational attention given to the work before the pandemic, the psychological and physiological impact of the pandemic on individual health to impede role performance, and the impact of the challenges/changes on the sense of self/identity. At the same time there is also a curiosity about the coping mechanisms they might have sought to counter the situations. I have curated the collection of narratives by inviting Phaptawan Suwannakudt (Australia and Thailand), Nidhi Agarwal (India), Monika Lin (China and United States), Tazeen Qayyum (Canada and Pakistan) and Arisa Chinen (Japan) to share their stories. Their artistic responses will be in the form of text and image.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".