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Record W3014933521 · doi:10.22215/cjers.v13i1.2550

Becoming European: Strangers Finding a Place in the European Union

2020· article· en· W3014933521 on OpenAlexvenueno aff
Maricia Fischer-Souan

Bibliographic record

VenueThe Canadian Journal of European and Russian Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeFeelingDialecticReflexivitySociologyEuropean unionMobilitiesIdentity (music)The SymbolicConsciousnessGender studiesEconomic geographyAestheticsGeographySocial psychologyPsychologyAnthropologyEpistemologyArtPsychoanalysis

Abstract

fetched live from OpenAlex

This article addresses the idea of belonging in Europe from the perspective of postcolonial migrants settling in EU societies. It draws on over one hundred in-depth interviews with Algerian, Ecuadorian, and Indian individuals settled mainly in and around the cities of London, Madrid, and Paris. Rather than investigating migrants’ orientations to Europe through a narrow interest in self-identification (feeling vs. not feeling European), it delves into individual migration narratives for evidence of how Europe is imagined (if it is imagined at all) during the migration process and its relation to other physical and symbolic sites. As a frame for interpreting individual migration narratives, I introduce the concept of ‘migratory rupture’, a dialectical experience of both the disorienting and creative aspects of migration. In excavating some of the reflexive processes involved in constructing symbolic geographies of attachment, I find that regardless of the scales of comparison used to articulate place affiliation across different contexts, e.g. whether small-scale (neighbourhoods or city districts) or larger-scale (supranational or de-territorialized categories), symbolic geographies allow migrants to view their transnational life experience on a single, coherent plane and express a form of global consciousness.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.079
GPT teacher head0.301
Teacher spread0.222 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of European and Russian StudiesSame topicMigration, Refugees, and IntegrationFrench-language works237,207