Bibliographic record
Abstract
This special section focuses on education in relation to diverse refugee groups by exploring and engaging interdisciplinary perspectives. A collection of nine articles articulates the plight of refugees in the resettlement context at the nexus of conflicts with host citizens, pre‐migration trauma and post‐migration stress, and educational opportunities for survival and upward social mobility. Refugees discussed in this special section come from different countries of origin, while facing similar challenges of integration in different host countries. Beginning with the context and common background of refugees, this editorial analyses the underlying mechanisms of anti‐refugee sentiments based on the host countries studied in the nine articles, including Australia, Canada, Greece, Kenya, the United Kingdom, the United States, South Korea, Sweden and Turkey. It then discusses the following themes derived from the nine articles: (1) the gap between refugees’ educational aspirations and opportunities; (2) refugees’ identity negotiation; and (3) educational practices, policies and leadership for refugees. Lastly, it synthesises the central arguments of the articles to give a sense of how anti‐refugee sentiments are interconnected with barriers to learning for refugees and to provide a rationale for institutionalising inclusive education for them. This special section is aimed at encouraging readers to adopt a multi‐layered lens for examining refugee education to better understand how refugees are not only traumatised victims of extremist ideologies, but also ostracised in a wide range of settings including schools, universities and communities in their host countries.
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 | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".