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Record W4394797096 · doi:10.1177/00380385241242041

National Assimilation and/or Cosmopolitan Transnationalism? Impending Transnationalism among the Upwardly Mobile Children of Refugees

2024· article· en· W4394797096 on OpenAlexaff
Aryan Karimi, Sara K. Thompson, Sandra M. Bucerius

Bibliographic record

VenueSociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of AlbertaToronto Metropolitan UniversityUniversity of British Columbia
Fundersnot available
KeywordsTransnationalismSomaliRefugeeSociologyMainstreamGender studiesMigration studiesPolitical sciencePolitics

Abstract

fetched live from OpenAlex

In sociology of migration, assimilation theory anticipates second-generations’ socio-economic mobility into the mainstream. In contrast, the concept of transnationalism predicts second-generations’ simultaneous belonging to the origin and destination countries. We draw on 118 qualitative interviews with second-generation Somali-Canadians whose parents were refugees to assess which of these perspectives best explains our participants’ experiences. We explore educational and occupational attainments and transnational practices. Our data show upward mobility and an absence of contemporary transnational practices. Yet, we find that our participants’ refugee background impacts their transnationalism; their parents’ forced departures as refugees and the ongoing violence in their origin-country lead to second-generation Somali-Canadians’ lack of transnationalism. Many, however, emphasize their desire to discover their origin-country at some point in the future. As such, to contribute to the emerging literature on second-generations with refugee parents, we argue that refugee background seems to push transnationalism into the future for our study participants.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
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 head0.016
GPT teacher head0.344
Teacher spread0.328 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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