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Record W4413926568 · doi:10.1080/17450101.2025.2549691

Not settlement but movement: Exploring mobility as central to the wellbeing of young people from migrant backgrounds building lives from rural Australia

2025· article· en· W4413926568 on OpenAlexaff
Meghan Lee, Debra McDougall, Zubaidah Mohamed Shaburdin, Karen Block, Cathy Vaughan

Bibliographic record

VenueMobilities · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsSettlement (finance)MobilitiesMovement (music)SociologyGender studiesGeographyEconomic geographyAestheticsSocial scienceBusiness

Abstract

fetched live from OpenAlex

Australian federal, state, and local governments have invested in international migration prevent population decline and promote economic development in regional and rural Australia. While geographic mobility is integral to life in many rural communities, policy and research approaches to ‘successful settlement’ frequently centre on insufficient ideals of stasis. Based on participatory research with young people from migrant and refugee backgrounds in regional and rural Australia, this paper explores everyday mobilities that were central to the wellbeing of this group, conceptualising successful regional and rural settlement as fundamentally mobile. We conceptualise mobility from a rural perspective, drawing from geographer Anne Buttimer’s ‘home and reach’ framework, and a racialized perspective, informed by phenomenological approaches to whiteness and racialized embodiment developed by Sara Ahmed and Helen Ngo (Ahmed Citation2007; Ngo Citation2017). For young people in this study, the familiar spaces of home were characterised by discomfort and existential precarity associated with ‘being not’ white, which drove them to reach for spaces of respite and existential security elsewhere. At the same time, iterative processes of return supported an embodied connection to home as the place where their relational lives were centred. Ultimately, the freedom to leave and return underpinned their possibilities for staying to build a meaningful, enduring sense of home.

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)0.005
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0060.006
Open science0.0010.010
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.318
Teacher spread0.275 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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