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Record W4401890330 · doi:10.1111/cag.12947

Immigrant labour, rural economies, and the question of housing

2024· article· en· W4401890330 on OpenAlexaffvenueabout
Bronwyn Bragg

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsImmigrationWorkforceUnderpinningLeverage (statistics)Immigration policyEconomic growthRefugeeLabour economicsBusinessDevelopment economicsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Abstract There is a renewed interest in policy initiatives that aim to ruralize and regionalize immigration in Canada. These efforts are visible through temporary foreign worker programs linked to Provincial Nominee Programs, as well as the increase in the number of refugee resettlement programs in smaller communities. Against this backdrop, cities in Canada are experiencing a housing crisis. There is a tacit assumption that immigrants in smaller and more rural settings will fare better with respect to housing. This paper examines the assumptions underpinning efforts to ruralize and regionalize immigration—namely that immigrants to smaller centres will find more affordable and available housing. By presenting two case studies of small towns that have successfully leveraged immigration to meet their labour market needs, this paper demonstrates that housing remains a critical issue for newcomers, even outside of large urban centres. Further, the paper argues that discussions about housing need to be linked to broader policy conversations about immigration and labour, especially in contexts where transnational corporations leverage immigration and temporary labour programs to secure their workforce, without parallel investments in infrastructure and housing.

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)0.001
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 score0.485
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
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 head0.005
GPT teacher head0.176
Teacher spread0.172 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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