Experiences of Immigrants During Disasters in the US: A Systematic Literature Review
Bibliographic record
Abstract
As a vulnerable population, immigrants can be disproportionately affected by disasters. Because of their legal and migratory status, immigrants may have different challenges, needs, and possibilities when facing a disaster. Yet, within disaster studies, immigrants are rarely studied alone. Instead, they are often considered part of the large heterogeneous group of racial and ethnic minorities in the United States. This racial classification points to a gap in the literature and in our understanding of how disadvantaged groups may cope with disasters. To address this gap, the current study hypothesizes that: (1) Immigrants have unique experiences and disaster impacts compared to the broader aggregated category of racial and ethnic minorities in the U.S. and (2) There are variations in disaster experiences and impacts across different types of immigrant subgroups beyond refugees. To explore these hypotheses, a study of the literature across six databases from 2018 to 2023was conducted. The review identified a total of 17 articles discussing immigrant experiences during disasters. Major cross-cutting themes on immigrant disaster experiences include fear of deportation, restrictive immigration status, excessive economic burden and labor exploitation, employment rigidity, adverse health outcomes, limited informational resources and limited social capital, selective disaster relief measures, and infrastructural challenges as regards to housing and transportation. Many of the themes identified are unique to immigrants, such as the fear of deportation, restrictive immigration status and visa policies, and selective disaster relief measures.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".