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Record W4367017281 · doi:10.4337/9781839106385.00020

Surveillance, police, and quarantining COVID-19 in Canada and Australia

2022· book-chapter· en· W4367017281 on OpenAlexaboutno aff
Randy K. Lippert, Adam Molnar

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarantineCoronavirus disease 2019 (COVID-19)PandemicSocial distancePolitical scienceSoftware deploymentDistancingCriminologyPublic relationsComputer securitySociologyEngineeringMedicineComputer science

Abstract

fetched live from OpenAlex

The global surveillance of the COVID-19 pandemic has seen most countries imposing traditional quarantine of populations supported by police surveillance and underpinned by obscure legal instruments. This chapter explores these strategies in two advanced democracies, Canada and Australia, that are emblematic of global patterns. The analysis reveals a peculiar amalgam of premodern strategies of quarantine coupled with cutting-edge police administered surveillance technologies involving enactments of legal instruments consistent with notions of 'counter-law' and 'exception'. The components of global surveillance involving police here are not especially unique but become so in emergency contexts when rapidly combined with new socio-technical capabilities. Here traditional quarantine becomes reconfigured, partially supported through police deployment, as a mobile, ever-shifting, mostly lateral form called 'social distancing'. The broader implications of our analysis for understanding current governing logics and global surveillance, the old strategy of quarantine involving police, and the dangers these surveillance-legal arrangements pose for a post-pandemic world are discussed.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.304
Teacher spread0.257 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2022
Admission routes1
Has abstractyes

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