Digital dilemmas in the (post-)pandemic state: Surveillance and information rights in South Korea
Bibliographic record
Abstract
Drawing on South Korea’s response to COVID-19, this article examines how the digital measures that were implemented by the nation state during the pandemic intensified the dilemma between public safety and information rights. South Korea’s highly praised handling of COVID-19 raises the question of how far digital technology can infiltrate everyday life for the sake of public safety and how citizens can negotiate the rapid digital transformation of a nation state. The South Korean government’s digital measures during the pandemic involved the extensive use of personal data; however, citizens were not allowed sufficient participation in the flow of information. By critically examining the South Korean case, this article reveals that the government coped with the pandemic through digital surveillance as a way to avoid physical lockdown, and in so doing, projected its desire for transition to a digitally advanced state while facilitating nationalism through a digital utopian discourse.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".