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.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".