There’s (not) an App for that: situating smartphones, Excel and the techno-political interfaces and infrastructures of digital solutions for COVID-19
Bibliographic record
Abstract
Abstract This paper focuses on the operational-infrastructural puzzles of mHealth via COVID-19 Contact Tracing Apps (CTA). Significant literature exists on user adoption of the platformisation of public health during the pandemic, but there has been limited consideration of how those responsible for implementing CTA design, deployment, and use of public health infrastructures did so. We redress this imbalance by exploring some of the politics and practicalities of offering CTA as technical ‘solutions’ to pandemic problems. Our work adds to previous comparative analyses of mHealth by drawing on data from key actors across government, industry, and civil society involved in designing and implementing CTA into public health across 5 jurisdictions: Australia, Canada, New Zealand, Singapore, and the United Kingdom. While CTA research often frames tensions around efficacy and adoption (e.g. privacy trade-off), we find hidden infrastructural tensions within a situation of political and technical constraints in the ‘back end’ of the platformisation of public health. The paper offers new insights to pandemic politics by shifting questions from digital contact tracing and pandemic surveillance interfaces to understanding CTA as infrastructures of public health. While CTA user-software interactions produce certain research questions, querying the infrastructural complexity of digital public health projects require and produce a different set of data and knowledge.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".