Gaps in the gaze: Informatic practice and the work of public health surveillance
Bibliographic record
Abstract
Many works that may be situated within the interdisciplinary field of Surveillance Studies have described dangerous potentialities associated with the pervasive, IT-mediated merger of once discrete data sets. In effect, these works cautioned about the rise of “big data” before it was named as such. Even so, they share an uncomfortable consonance with euphoric claims about the revolutionary transformation portended by big data. Situating both euphoric and critical accounts of the IT-mediated gaze within a larger informatic ethos — a spirit in the Weberian sense of this term, defined above all by its concealment of the labor that makes IT work — this article argues that discourse on the data-driven, information revolution must be supplemented by a more modest discourse empirically rooted in the everyday, pragmatic realities of IT. Where it departs from well-established social scientific analyses of IT, however, is in its development of a novel concept: informatic practice. Informatic practice may be defined as the sum of labor or activity that materializes information, including, for instance, such mundane activities as data entry. To empirically illustrate some complexities associated with informatic practice, this article discusses process challenges associated with the implementation of a large-scale (or “big”), regionally interconnected public health information system in Ontario, Canada. Informed by science and technology studies (STS) and actor-network theory (ANT), it uses documentary evidence and interviews with 38 key informants to describe informatic practice and to illustrate the mutations—the natural change—introduced into the IT-mediated gaze by everyday, material practices. This complicates both critical and euphoric claims about big data.
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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 | 0.103 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.042 | 0.190 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".