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 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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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; a candidate call from one teacher head, 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".