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Record W4361011120 · doi:10.26522/ssj.v17i1.4006

Acts of Citizenship in Time and Space among Agricultural Migrant Workers in Quebec during the COVID-19 Pandemic

2023· article· en· W4361011120 on OpenAlexaffvenueabout
Guillermo Candiz, Tanya Basok, Danièle Bélanger

Bibliographic record

VenueStudies in Social Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité LavalUniversity of Windsor
Fundersnot available
KeywordsCitizenshipMigrant workersAgency (philosophy)RepatriationPandemicSpace (punctuation)Coronavirus disease 2019 (COVID-19)AgricultureWork (physics)Economic growthFarm workersPolitical scienceSocioeconomicsSociologyBusinessDemographic economicsGeographyLawEconomics

Abstract

fetched live from OpenAlex

Migrant farm workers recruited under Canada’s temporary employment programs work in difficult environments, under poor working conditions, and live in unsafe housing in remote rural communities. Fearful of repatriation or replacement, many accept their working and living conditions as part of a necessary sacrifice to improve their living conditions and those of their families in the countries of origin. At the same time, some migrant farm workers assert their agency by escaping from farms, subverting regulations, or challenging various forms of discipline used to control their bodies and activities. Following Isin and Nielsen (2008), we refer to these actions as “acts of citizenship.” Drawing on research conducted among migrant farm workers during the COVID-19 pandemic in the province of Quebec, Canada, we situate these acts, particularly the tendency to escape from abusive and exploitative working relationships, in a particular space and time shaped by the COVID-19 pandemic. More specifically, we demonstrate the link between these acts and certain conditions and opportunities that arose at that time, such as increased support for migrant farmworkers by a non-governmental organization and the facilitation of movement of migrant farmworkers across the Canada-U.S.-border by the “migration industry.”

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)0.002
metaresearch head score (Gemma)0.003
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 score0.619
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
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 head0.075
GPT teacher head0.372
Teacher spread0.297 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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