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Record W2108666651 · doi:10.48416/ijsaf.v19i1.237

Migrant Workers and Changing Work-place Regimes in Contemporary Agricultural Production in Canada

2020· article· en· W2108666651 on OpenAlexaffabout
Kerry Preibisch

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFood systemsAgricultureGlobalizationImmigrationContext (archaeology)Corporate governanceWork (physics)Political scienceFood processingProduction (economics)Food securityEconomic systemEconomicsEconomic growthPolitical economyMarket economyGeography

Abstract

fetched live from OpenAlex

Contemporary processes of globalization have had significant implications for food systems around the world. The adoption of neo-liberal policies on a global scale, changing systems of governance in supply chains, and the development of new technologies have transformed how food is produced and consumed. Although the implications of these changes for the labour sustaining agri-food systems have received scant attention in the literature, research suggests they are profound. In this article, I seek to further our knowledge of how these processes are unfolding in a high income country context through a focus on Canada, examining in particular how changes to immigration policy have rendered work in Northern agri-food industries more precarious. In so doing, I seek to contribute to theoretical debates on the role of the state in regulating work-place regimes and managing capitalist accumulation in agriculture.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.059
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.231
Teacher spread0.188 · 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 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

Citations56
Published2020
Admission routes2
Has abstractyes

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