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Record W4394833130 · doi:10.1111/cag.12917

Settling beyond big cities: A scoping review of the Canadian literature on immigration to rural and smaller communities

2024· review· en· W4394833130 on OpenAlexafffundvenueabout
Stacey Haugen, Rachel McNally, Lars Hällström

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Lethbridge
FundersGovernment of Canada
KeywordsImmigrationDiversity (politics)GeographySettlement (finance)Political sciencePublic policyEconomic growthGeographic mobilityRural areaPopulationRegional scienceEconomic geographySociologyBusiness

Abstract

fetched live from OpenAlex

Abstract Newcomers are living and working across rural and smaller communities in Canada. However, immigration research and policy are overwhelmingly focused on large, urban centres. Responding to this knowledge gap, this article presents the results from a scoping review of the Canadian literature on immigration outside of Canada's largest cities. An analysis of 90 studies reveals several key trends in the literature related to the geographic focus and themes addressed. The results of the review demonstrate that the majority of studies focus on regions with a high population density that are in close proximity to major urban centres, thus revealing a gap in knowledge regarding settlement across more rural and northern parts of the country. Issues related to settlement services, employment opportunities, welcoming communities, public policy, infrastructure, and retention and secondary migration were the most addressed themes across the literature and represent the diversity of rural Canada. In response to these findings, we conclude with a discussion of the potential opportunities for future research and policy change .

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.008
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.232
Teacher spread0.213 · 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.

Study designOther design
Domainnot available
GenreReview

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
Published2024
Admission routes4
Has abstractyes

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