Dis-ease Surveillance: How Might Surveillance Studies Address COVID-19?
Bibliographic record
Abstract
We are currently in the midst of a global pandemic with the spread of Coronavirus Disease 2019 (COVID-19). While we do not know how this situation will unfold or resolve, we do have insight into how it fits within existing patterns and relations, particularly those pertaining to sociocultural constructions of (in)security, vulnerability, and risk. We can see evidence of surveillance dynamics at play with how bodies and pathogens are being measured, tracked, predicted, and regulated. We can grasp how threat is being racialized, how and why institutions are flailing, and how social media might be fueling social divisions. There is, in other words, a lot that our scholarly community could add to the conversation. In this rapid-response editorial, we provide an introduction to the framing devices of disease surveillance and discuss how a surveillance studies orientation could help us think critically about the present crisis and its possible aftermath.
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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 | 0.053 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.021 | 0.028 |
| 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".