On Digital Models: Responding to Viral Metaphors in Pandemic Times
Bibliographic record
Abstract
COVID-19 has been a crisis represented and interpreted through models. Models are metaphors that illustrate one phenomenon in and through another that is better understood or seemingly more transparent. In this article, we consider digitally driven COVID-19 models that draw on the certainty of data from smartphones and social networks to make predictions about a poorly understood virus. Network data normally used to model information spread drive models of an actually existing biological virus. A return to HIV network models of the 1980s helps map the social implications of this latest turn to modeling. These earlier models were used to hone stigmatizing viral metaphors about behavior, risk, and exposure, in the shadow of an emerging digital culture. Thinking across COVID-19 and HIV modeling demonstrates how models can support personal responsibilization, be used to blame “bad” actors, and justify the creep of new surveillance practices under the rubric of “Data for Good” programs. Drawing on critical HIV and queer studies, we argue that the people and behaviors that are opaque to viral models and their methods of capture present potential avenues for speaking back to digital virality’s terms. We highlight these exceptions, which show how certain lives make trouble for models and their sensibilities, telling of queer forms of life, desire, and contact that evade modeling altogether.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".