Bibliographic record
Abstract
Family-related migration has been the main channel of legal entry to many major immigrant-receiving countries since the 1970s (Kofman, 2004). In 2005, around 40–60 per cent of long-term migrants in these countries (except Japan and the UK) were actually family-related migrants (International Organization for Migration [IOM], 2008, p. 157). The number of children among them is difficult to determine (White, Loire, Tyrrell, & Carpena-Méndez, 2011, p. 1160), but the prevalence of family-related migration in many countries today strongly suggests the presence of child migrants (accompanied or non-accompanied). These young people originate from various countries, mostly in the global South. They move not only to classical immigration countries, such as the US and Canada, but also to newly emerging ones, like Italy and Japan. In their destination country, they are compelled to deal with a variety of ‘contexts of reception’ (Portes & Rumbaut, 2001): the immigration policy, the school system, the immigration history of their ethnic group and its position in the existing social order, and so on. The plurality of places of origin and destination of these young migrants underscores their diversified and differentiated ‘routes’ (Clifford, 1997), which raises significant empirical, theoretical, and methodological questions. How does family-related migration affect the life trajectories, sociality, and identity of children? How can we capture the lived experiences of young migrants who have undergone childhoods within two or more different social settings due to migration? In what way should we approach their childhoods thus characterized by individual and familial mobilities? These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.490 | 0.302 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".