Refugee education: Introduction to the special section
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it