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Record W2525396298 · doi:10.29173/cjs28256

Contentious mobilities and Cheap(er) Labour: Temporary Foreign Workers in a New Brunswick Seafood Processing Community

2016· article· en· W2525396298 on OpenAlexaffvenueabout
Christine Knott

Bibliographic record

VenueThe Canadian Journal of Sociology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsEconomic shortageMigrant workersUnemploymentMobilitiesEthnographyMeat packing industrySociologyLabour economicsEconomic growthEconomicsPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Canada’s Temporary Foreign Worker Program (TFWP) is highly contentious. Particularly contentious are those parts of the program that have allowed for exploitative labour practices and the replacement of Canadian workers. Mobility for employment has been increasing, and researchers have focused on different types of mobile workers ranging from international (including the TFWP) to intra-provincial migrants, often in isolation from each other. Less research has focused on multiple mobilities within one industry to understand how and why labour force composition and employee mobility patterns change over time. Also under researched is why demand exists for TFWs in areas with high unemployment. This paper uses a case study of the seafood processing industry (both wild and farmed) in a rural region of New Brunswick to explore this industry’s claims about labour shortages and serial reliance on differently mobile labour forces over time. It draws on findings from a review of relevant documents and ethnographic fieldwork including interviews. Using the historical changes in the (im)mobility patterns of processing workers in this region, this paper highlights how the increased use of the TFWP by seafood processing companies is tied to manufactured raced and gendered employer practices.

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)0.000
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 score0.402
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.050
GPT teacher head0.283
Teacher spread0.233 · 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

Citations13
Published2016
Admission routes3
Has abstractyes

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