Bibliographic record
Abstract
This chapter analyzes how gender interfaces with xenophobic and Islamophobic bias to exclude young males from Muslim-majority countries from third country refugee resettlement programs under the 1951 Refugee Convention. It explores how the narratives around Islamophobia, masculinity, and gender norms affect if a person is classified as vulnerable and in need of third country refugee resettlement. The chapter provides the historical and legal backdrop necessary to understand that state’s interests are intertwined in the “refugee” definition and how vulnerability is legally conceptualized. It examines how the United Nations High Commissioner for Refugees (UNHCR), Germany, Canada, and the United States have constructed priority categories based on vulnerability for third country resettlement. Refugee resettlement is one of the UNHCR’s durable solutions to refugee flows. A durable solution is one that assists refugees to live safely while rebuilding their lives. Males who conform to gender norms from Muslim-majority countries are excluded from the protections of the Refugee Convention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".