Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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 it