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Record W7125529254 · doi:10.1108/978-1-80592-265-0

Families and Migration

2025· book· en· W7125529254 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationMeaning (existential)RefugeeUkrainianHuman migrationMigration studiesImmigration policySettlement (finance)Field (mathematics)

Abstract

fetched live from OpenAlex

Families and Migration: Examining the Human Meaning of Migration provides a broad examination of immigrant families, with a particular focus on examining the human meaning of migration. Chapters analyze the complex interplay between migration and family dynamics across various global contexts, addressing key themes such as integration, cultural identity, mobility aspirations, and caregiving. This edited collection explores diverse case studies, including the integration perspectives of Ukrainian refugees in East Germany and the theoretical frameworks for understanding the family-migration nexus. It delves into the cultural significance of traditional clothing across generations, the mobility aspirations of young migrant descendants in Portugal, and the settlement experiences of a Bangladeshi family in Canada through arts-based ethnography. Further, the authors examine migration trends, policy implications, and well-being among transnational families in North America, and the caregiving experiences of Polish immigrants caring for individuals with Alzheimer's disease. Offering significant contributions to the field of migration studies by providing a nuanced understanding of how migration shapes and is shaped by family dynamics, Families and Migration: Examining the Human Meaning of Migration is an essential resource for anyone interested in the socio-cultural impacts of global mobility and the intricate dynamics of migrant families.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.017
GPT teacher head0.294
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreOther

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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