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Record W4401778402 · doi:10.1080/1369183x.2024.2393652

Friendly, not friends: migrant settlement and diverse social ties in Australian regional cities

2024· article· en· W4401778402 on OpenAlexfundno aff
Bronte Alexander, L. Rivera, Rebecca Wickes

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEthnic groupMulticulturalismSettlement (finance)Economic geographyInterpersonal tiesSociologyValue (mathematics)TransnationalismGender studiesNarrativePolitical scienceGeographySocial scienceBusinessLaw

Abstract

fetched live from OpenAlex

Visa policies and pathways are increasingly driving international migration to Australia’s regional and rural centres. Often embedded within a multicultural imaginary, notions of friendliness and neighbourliness are instilled in the ‘small town’ narratives of these regional cities. However, little is known about whether this friendliness translates to the formation of deeper, meaningful connections that create the conditions for a sense of belonging to emerge. Drawing on empirical data from two regional cities across Queensland, this research investigates the challenges and barriers to forming friendships between migrant communities and ‘local’ Australian residents. We explore how co-ethnic relationships, while significant, do not always benefit and bolster migrants’ sense of belonging in the regions. Instead, we argue that inter-ethnic friendships with other migrant communities offer pathways to belonging in regional contexts. In doing so, we challenge prevalent assumptions regarding the inherent value of co-ethnic ties and the tensions so often raised within inter-ethnic relations.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.114
GPT teacher head0.402
Teacher spread0.288 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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