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Record W4391659188 · doi:10.32920/25193468

Precarious Mobility and the COVID-19 Pandemic: Re-Examining Canada’s Migration-Security Nexus

2024· preprint· en· W4391659188 on OpenAlexaffabout
V. Toma

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsThe King's UniversityToronto Metropolitan University
Fundersnot available
KeywordsNexus (standard)RefugeePandemicCitizenshipCoronavirus disease 2019 (COVID-19)Political scienceState (computer science)Development economics2019-20 coronavirus outbreakDemocracySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GeographyEconomic growthPolitical economySociologyEconomicsOutbreakPoliticsLawMedicineVirology

Abstract

fetched live from OpenAlex

It is an understatement to express that the COVID-19 pandemic has devastated the state of our world, with currently over 4 million confirmed deaths and over 200 million cases worldwide. One of the most predominantly impacted groups by the conditionalities of the coronavirus has been precarious status migrants, such as asylum seekers, refugees, and temporary foreign workers. The pandemic has triggered many securitized Western nation states to further fortify their borders. While citizenship regimes such as Canada claim to have an actively implemented “closed border policy”, the only individuals that have truly been systemically excluded from entering the country have been those with precarious status. This piece will revisit the Canadian migration-security nexus through a COVID-19 lens and will argue that the marginalization of precarious bodies and their mobility only reproduces the existing practices of the Canadian state to exclude refugees and migrants, using the pandemic as a justification to exercise exceptional power to securitize against them as the “undesirable” other.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.167
Threshold uncertainty score0.966

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0260.012
Scholarly communication0.0110.003
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.000

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 head0.135
GPT teacher head0.425
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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 routes2
Has abstractyes

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