Sexual and Gender Minority Immigrants, Refugees, and Asylum Seekers
Bibliographic record
Abstract
Over the years, research on immigrants, refugees, and asylum seekers has been rooted in hetero- and cisnormative discourse that has not only rendered these individuals invisible but also led to a lack of understanding of what they need to successfully integrate into host societies. However, as significant numbers of sexual and gender minority (SGM) people continue to migrate from their countries of origin to various countries around the world, SGM migration can no longer be viewed as a niche area, but one that is placed front and center in migration studies and current discourse on immigration. To provide context for the articles, books, and chapters in this article, it is important to discuss the differences among the terms: immigrant, refugee, and asylum seeker. An immigrant is a broad term used to describe someone who permanently moves to a foreign country. A refugee refers to an individual who flees their country of origin due to persecution; however, refugees are vetted prior to entering the host country while waiting in a country of transit. For example, the United Nations High Commissioner for Refugees might vet SGM individuals in Turkey before sending them to Canada. An asylum seeker also flees their country of origin due to persecution but is vetted after entering the host country. For instance, an SGM individual may enter the United States on a student or tourist visa, but then decide to seek asylum because they fear returning to their country of origin. The other term that is commonly used is migrant, which refers to an individual who moves from one place to another, either temporarily or permanently, in search of better work opportunities or social conditions. While domestic migration does occur often, this review focuses specifically on international migrants (i.e., those who move to a foreign country). The term migrant is used interchangeably with immigrant, refugee, or asylum seeker. Although these categories are technically different, it can be hard to distinguish among these socially constructed categories, as the experiences of immigrants, refugees, and asylum seekers share many commonalities during the pre-migration, travel, and resettlement phases, as will be evidenced in the works discussed in this article.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".