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Record W2912050642 · doi:10.1177/0896920518820936

Work, Social Reproduction, the Transnational Household, and Refugee Resettlement: A Canadian Case Study

2019· article· en· W2912050642 on OpenAlexaffabout
Ray Silvius

Bibliographic record

VenueCritical Sociology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsRefugeeReproductionSocial reproductionSituatedSociologyWork (physics)Economic growthPolitical scienceEconomicsSocial scienceLawSocial capital

Abstract

fetched live from OpenAlex

This article puzzles out the relationships between displaced peoples, their families, resettlement, the household, employment, and social reproduction – the often voluntary, feminized, and un- or under-compensated labour that reproduces a family, household, or labour force. Transnational refugee households and the conditions of refugee resettlement are co-constituted. These households are: 1) physical dwellings where a family is situated; 2) sites of emotional, care, and additional work required for the reproduction of the (often transnational) family; 3) necessitating ‘material’ inputs, in the form of paid labour, social provisions, or other sources. This article uses Social Reproduction Theory (SRT) to demonstrate the tradeoffs in securing the economic and care resources required for maintaining transnational refugee households. Profiling cases of resettling refugee families in Winnipeg, Canada, the article suggests that ‘small-n’ research reveals the challenges in meeting common resettlement imperatives amid expensive housing markets and restricted access to social and economic resources.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.472

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0380.011
Scholarly communication0.0030.002
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.351
Teacher spread0.301 · 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 designQualitative
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

Citations4
Published2019
Admission routes2
Has abstractyes

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