Introduction: Linguistic Disadvantage in Diversifying and Restructuring Welfare Societies
Bibliographic record
Abstract
This themed section explores linguistic disadvantage as a key form of structural-institutional disadvantage in welfare societies, focusing on how language policies, practices, and ideologies shape migrant background service users’ access to services, rights, and social protection. The novel contribution by the team of authors with a background in social policy, social work, sociology, and ethnology is to fill existing conceptual and empirical gaps by advancing a relational view on multilingualism and linguistic diversity and highlighting the critical importance of language in institutional policies and practices and in the everyday encounters and relationships between service users, the welfare state and its representatives. The articles represent a rich variety of national and cross-national research, drawing on empirical case studies from Finland, Sweden, Belgium, and Canada. By situating language practices within broader social, political, and cultural struggles, the section calls for social policies and practices that challenge monolingual ideals and promote plurilingual ways of knowing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".