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Record W1869859178 · doi:10.1111/cag.12067

Negotiating belonging following migration: Exploring the relationship between place and identity in Francophone minority communities

2014· article· en· W1869859178 on OpenAlexaffvenueabout
Suzanne Huot, Belinda Dodson, Debbie Laliberté Rudman

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsWestern University
Fundersnot available
KeywordsNegotiationFrenchIdentity negotiationMulticulturalismSociologyIdentity (music)ImmigrationGender studiesNeuroscience of multilingualismEthnographyContext (archaeology)Focus groupQualitative researchPolitical sciencePsychologyAnthropologySocial scienceLinguisticsGeographyPedagogyAestheticsLaw

Abstract

fetched live from OpenAlex

Abstract A qualitative study was conducted within the Francophone minority community (FMC) of London, Ontario to explore the integration experiences of French‐speaking immigrants from visible minority groups. We address how shifts to place and identity experienced following international migration influenced the study participants' negotiation of belonging within the host community. The ethnographic approach to research was guided by a theoretical framework drawing on geographical and sociological literature critically attending to power and place. Findings focus upon the negotiation of two key tensions influencing belonging. First we address the tension between Canada's official bilingualism and the Francophone immigrants' lived bilingualism within the local FMC. We then discuss the research participants' everyday experiences of displacement and exclusion as embedded within a context of official multiculturalism. The findings serve to illustrate ways in which belonging is negotiated in relation to the politics of place.

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)0.007
metaresearch head score (Gemma)0.006
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 score0.675
Threshold uncertainty score0.646

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0240.018
Scholarly communication0.0060.003
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.252
Teacher spread0.226 · 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

Citations44
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Geographies / Géographies canadiennesSame topicMigration, Refugees, and IntegrationFrench-language works237,207