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Record W3131282750 · doi:10.1002/psp.2443

Spaces of well‐being and regional settlement: International migrants and the rural idyll

2021· article· en· W3131282750 on OpenAlexaff
Natascha Klocker, Paul Hodge, Olivia Dun, Eliza Crosbie, Rae Dufty‐Jones, Celia McMichael, Karen Block, Margaret Piper, Emmanuel Musoni, Lynda L. Ford, Carly N. Jordan, D. C. Radford

Bibliographic record

VenuePopulation Space and Place · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsGolder Associates (Canada)
FundersDepartment of Health, State Government of VictoriaUniversity of Wollongong
KeywordsResidenceRegionalisationSettlement (finance)GeographyIdyllRural settlementPopulationHuman geographyEconomic growthRural areaEconomic geographyPolitical scienceSociologyEconomicsDemography

Abstract

fetched live from OpenAlex

Abstract Regionalisation is a hallmark of Australia's approach to international migration, reflecting governments' growing concern with where new arrivals live. Residence in regional Australia is encouraged (mandated, for some visas) in response to urban population pressures alongside rural population and economic decline. Parallel to regionally focused visa schemes exists a pattern of voluntary urban‐to‐rural migration among some international migrants. Such secondary mobility counters the policy logic that international migrants only live outside cities when required to do so. This paper explores 18 African migrants' motivations for ‘urban flight’: Australian cities have failed to sustain their well‐being and they consider rural life a remedy. Their preference for rural locations is not purely instrumental, it is shaped by deep‐seated affective connections. Given the challenges of regional population retention, settlement policies should be recalibrated to support the aspirations of international migrants who feel an affinity for rural places, rather than compelling the rural settlement of others who do not.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0040.002
Open science0.0000.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.281
Teacher spread0.269 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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