STITCHING THE CURVE: PANDEMIC CRAFT AND FEMINIST DATA VISUALIZATION
Bibliographic record
Abstract
Feminist scholars are increasingly drawing attention to the ways “big data” and data representations reinscribe gender and racial inequality, an issue made even more pressing by the role data has taken in our daily lives since the start of the COVID-19 pandemic. "Stitching the Curve," a knitted pandemic data visualization project by librarians at the University of Alberta, offers an intersection between digital activism and craftivism, enabling a material, feminist response to an erasure and minimization of collective loss. We examine the media coverage around the project, which includes the online blogs of the project’s participants. Using critical technocultural discourse analysis (CTDA) as a guiding methodology, we consider simultaneously the feminist, activist framing and the influence of material and digital platforms on the cultural influence of the work (Brock 2018). Blogging and knitting are frequently associated with craft and writing as an expression of the domestic and personal, relegated to a feminine and, consequently, minimized space of care and labor. Through a critical technocultural discourse analysis of Stitching the Curve, we understand how the project makes a powerful statement in representing not only the oft-dismissed human cost of the COVID-19 pandemic, but also uses mediums of representation that challenge patriarchal “big data” collection and representational practices. Stitching the Curve makes data visualization a rhetoric of care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".