Bodily surveillance: Singapore’s COVID-19 app and technological opportunism
Bibliographic record
Abstract
Singapore won early kudos for its ‘gold standard’ handling of the COVID-19 pandemic back in February 2020. It was praised globally for its ability to activate an effective contact tracing system. Riding on this success, the government introduced ‘TraceTogether’, a mobile phone app to enhance contact tracing efforts, using a technology that leverages the Bluetooth feature on smartphones to track proximity between users and record their physical encounters. This paper contends that the roll-out of the app is a form of ‘technological opportunism’ to enhance greater bodily surveillance over its citizens during a time of crisis. The low number of downloads of the app initially (at 20%), before persuasion-coercion strategies were applied to lift the take-up rate to 90%, belies the assumption that surveillance is genuinely widely accepted. This paper details key responses to the app in Singapore, and the government’s decision to make it mandatory during the heart of the pandemic between 2020 to 2022. It considers the implications of technological opportunism, taking advantage of a pandemic to continue in the journey of turning citizens into what Michel Foucault would refer to as ‘subjectified bodies’ to be traced, tracked and codified.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".