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Record W6907267278 · doi:10.20381/ruor-30377

The Stranger and the K-Quarantine: Foreigners and Pandemic Response in South Korea

2024· article· en· W6907267278 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicGovernment (linguistics)Context (archaeology)Public healthParticipant observationSubjectivityContainment (computer programming)Coronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

From the early phases of the pandemic, the South Korean response to COVID-19 has garnered widespread international acclaim. This thesis explores the COVID-19 measures that were implemented in Korea throughout the pandemic in its entirety, that is, from the early successes of 2020, up until the radical lifting of mandates in the spring of 2022. This thesis documents and analyzes the state's COVID-19 containment strategy from the specific vantage point of foreigners living in Korea, a minority group that has been problematized in particular ways during global health crises like COVID-19. I draw upon fieldwork conducted in Seoul from 2021 to 2023, which included semi-structured interviews, participant observation, and informal conversations. Overall, I suggest that the state's technology-mediated virus mitigation strategy contributed to the production of individuated, disciplined subjectivity. In order to situate the COVID-19 response within larger sociocultural and historical landscapes, I begin by examining how previous health regimes sought to manage and control contagion in Korea, before turning my attention to the pre-emptive quarantine that was mandated for all international arrivals. Subjectivity in the context of COVID-19 is also investigated, by comparing my experiences living in Quebec and Seoul during the pandemic. Finally, I analyze COVID-19 public health messaging, focusing on the dissemination of COVID-19 information through emergency text alerts and government websites, as well as printed posters.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.231
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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