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Record W3197911323 · doi:10.32920/23739360.v1

The Political Economy of a Modern Pandemic: Assessing Impacts of COVID-19 on Migrants and Immigrants in Canada

2023· article· en· W3197911323 on OpenAlexaffabout
John Shields, Zainab Abu Alrob

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsImmigrationPandemicRacializationCoronavirus disease 2019 (COVID-19)PoliticsPolitical scienceNeoliberalism (international relations)Development economicsInequalityPolitical economyImmigration policyDemographic economicsEconomic growthSociologyEconomics

Abstract

fetched live from OpenAlex

<p>This paper explores the COVID-19 crisis with a focus on immigration and migration in Canada using a political economy lens. Neoliberalism has played a major role in shaping pandemic impacts and the responses to it. We critically assesses the deep structural inequalities that have caused disproportionate COVID-19 impacts on migrants and immigrants. Migrants and immigrants carry the unequal burden of COVID-19 because of racialization, labour precariousness, and exposure to health risks on job sites and in the poor neighborhoods and over crowded housing in which many live in. Mobility and borders have also been cast as a particular threat during the pandemic even though domestic sources are the main sources of contagion. We examine the use of borders as filtering mechanisms during COVID-19 and the negative impacts this has had on migrant populations. While crises like pandemics pose many dangers they also open up policy windows through which progressive change may be realized. We reflect on these possibilities.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.068
GPT teacher head0.303
Teacher spread0.235 · 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 designObservational
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

Citations11
Published2023
Admission routes2
Has abstractyes

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