Theorizing Age and Generation in Migration Contexts: Towards Social Age Mainstreaming?
Bibliographic record
Abstract
This paper proposes social age as an analytical framework within which to understand and respond to age and generation in migration contexts. It argues that comprehensive social age analysis can lead to greater age sensitivity in migration research, policy and programming. As such, this introduction situates the empirical studies in the special issue and draws on them to test, challenge and inform the social age analytical framework. Le cadre analytique que nous proposons dans cet article pour comprendre et trouver des réponses à la question des âges divers et des générations dans des contextes de migration est l’âge social. Nous soutenons qu’une analyse compréhensive de ce dernier peut mener à une plus grande sensitivité envers la question de l’âge dans la recherche, la politique et la programmation sur la migration. En tant que telle, cette introduction donne dans ce numéro spécial une place aux études empiriques, dans lesquelles elle s’appuie pour tester, mettre au défi et alimenter le cadre analytique de l’âge social.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".