Special Focus on Age Discrimination in Forced Migration Law, Policy, and Practice
Bibliographic record
Abstract
This special focus of Refuge highlights the widespread but under researched occurrence of age discrimination in forced migration law, policy, and practice. Using a conceptual lens of social age, authors analyze the ways in which people in situations of forced migration are treated differently on the basis of chronological age, biological development, and family status. By framing this differential treatment as discrimination, this special focus approaches age as an equity issue. Such an approach differentiates the articles presented here from other recent scholarship on specific age groups, which is framed largely in terms of their vulnerabilities and needs. This special focus is intended to stimulate further research and activism on age discrimination in all its forms in varying contexts of forced migration. L’accent particulier accordé à ce sujet dans Refuge souligne l’incidence généralisée, bien qu’insuffisamment étudiée, de la discrimination fondée sur l’âge dans la législation, la politique et la pratique concernant la migration forcée.À l’aide de l’optique théorique de l’âge social, les auteurs abordent une analyse du traitement différencié accordée aux personnes en situation de migration forcée en fonction de leur âge chronologique, de leur développement biologique et de leur statut familial. En considérant ces différences dans le traitement par l’entremise du cadre de la discrimination, l’âge est conçu en tant qu’enjeu d’équité dans l’optique de cette approche particulière. Une telle approche dans les articles présentés ici se démarque des travaux et recherches récentes sur les groupes d’âge spécifiques qui se conceptualisent plutôt en fonction des vulnérabilités et besoins des sujets concernés. Cette approche particulière vise à inciter des recherches ultérieures ainsi que des activités politiques concernant la discrimination fondée sur l’âge dans toutes ses manifestations dans les divers contextes de la migration forcée.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".