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Record W6980316022

Borders and boundaries in the lives of migrant agricultural workers

2020· article· en· W6980316022 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2020
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipPoliticsSocial exclusionAgriculturePopulationHealth careNormalization (sociology)Face (sociological concept)Public health
DOInot available

Abstract

fetched live from OpenAlex

In 2018, roughly 72%of the 69,775 temporary migrant agricultural labourers arriving in Canada participated in the Seasonal Agricultural Workers Program (SAWP). Despite having legal status in Canada, these individuals are often systematically excluded from community life and face barriers when accessing health and social services. SAWP workers’ exclusion from many public spaces and their incomplete access to the benefits of Canadian citizenship or residency provide us a unique opportunity to examine social and political mechanisms that construct(in)eligibility for health and protection in society.As individuals seeking to care for the sick and most marginalized, it is important for nurses to understand how migrant agricultural workers are positioned and imagined in society. We argue that the structural exclusion faced by this population can be uncovered by examining:(1)border politics that inscribe inferior status onto migrant agricultural workers;(2) nation-state borders that promote racialized surveillance, and;(3) everyday normalization of exclusionary public service practices. We discuss how awareness of these contextual factors can be mobilized by nurses to work towards a more equitable health services approach for this population.

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)0.000
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 score0.005
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.115
GPT teacher head0.346
Teacher spread0.231 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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