Bibliographic record
Abstract
Purpose Traditional public health methods for tracking contagious diseases are increasingly complemented with digital tools, which use data mining, analytics and crowdsourcing to predict disease outbreaks. In recent years, alongside these public health tools, commercial mobile apps such as Sickweather have also been released. Sickweather collects information from across the web, as well as self-reports from users, so that people can see who is sick in their neighborhood. The purpose of this paper is to examine the privacy and surveillance implications of digital disease tracking tools. Design/methodology/approach The author performed a content and platform analysis of two apps, Sickweather and HealthMap, by using them for three months, taking regular screenshots and keeping a detailed user journal. This analysis was guided by the walkthrough method and a cultural-historical activity theory framework, taking note of imagery and other content, but also the app functionalities, including characteristics of membership, “rules” and parameters of community mobilization and engagement, monetization and moderation. This allowed me to study HealthMap and Sickweather as modes of governance that allow for (and depend upon) certain actions and particular activity systems. Findings Draw on concepts of network power, the surveillance assemblage, and Deleuze’s control societies, as well as the data gathered from the content and platform analysis, the author argues that disease tracking apps construct disease threat as omnipresent and urgent, compelling users to submit personal information – including sensitive health data – with little oversight or regulation. Originality/value Disease tracking mobile apps are growing in popularity yet have received little attention, particularly regarding privacy concerns or the construction of disease risk.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".