Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".