The TraceTogether Matrix Has You – Surveillance, Rationalisation and Tactics of Governance in Singapore’s COVID-19 App
Bibliographic record
Abstract
In the heat of the COVID-19 pandemic, Singapore rolled out TraceTogether; a contact-tracing mobile app that uses proximity sensing to track the movements of its population. TraceTogether was initially voluntary, and used solely for contact tracing. By December 2020, the system became mandatory. This sparked a mass adoption that made TraceTogether possibly the most successful application in Singapore’s Smart Nation initiative. When it emerged in January 2021 that the data had been used by the police for criminal investigation, images of a totalitarianism sprang to mind, where technology permits the state an invasive awareness of the movement of individuals. In this paper, we defer from common arguments that Singaporeans are intrinsically trusting of the government or have been conditioned to accept ‘Big Brother’ modes of surveillance. Instead, we argue that the success of TraceTogether reflects a Singapore society that, through the rationalisation of surveillance, willingly participates in their own surveillance. In uncovering the genealogy of media discourse that surrounds TraceTogether, we highlight that it is the regular practice of voluntary surveillance, of subscribing oneself to the apparatuses of state control, rather than specific technologies, that characterises the Singapore surveillance state. We describe a matrix of reason, layered-on and normalised through media discourse, that exemplifies what Foucault has termed ‘governmentality’, which asserts a government’s power of control not over, but within, citizens.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".