Doing nothing does something: Embodiment and data in the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic redefines how we think about the body, physiologically and socially. But what does it mean to have and to be a body in the COVID-19 pandemic? The COVID-19 pandemic offers data scholars the unique opportunity, and perhaps obligation, to revisit and reinvent the fundamental concepts of our mediated experiences. The article critiques the data double, a longstanding concept in critical data and media studies, as incompatible with the current public health and social distancing imperative. The data double, instead, is now the presupposition of a new data entity, which will emerge out of a current data shimmer: a long-sustaining transition that blurs the older boundaries of bodies and the social, and establishes new ethical boundaries around the (in)activity and (im)mobility of doing nothing to do something. The data double faces a unique dynamic in the COVID-19 pandemic between boredom and exhaustion. Following the currently simple rule to stay home presents data scholars the opportunity to revisit the meaning of data as something given, a shimmering embodied relationship with data that contributes to the common good in a global health crisis.
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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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.115 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.001 | 0.026 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".