Uncanny and Displacement: Forcibly Displaced People Living in the State of Uncanny Amid the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic caused a massive humanitarian crisis across the globe. In times of emergency response, such as the pandemic, forcibly displaced people are among the most vulnerable groups who often face socio-economic marginalization, and other forms of intersecting oppression and discrimination, such as xenophobia and racism. In refugee camps, they are more susceptible to contracting the virus because of their poor living conditions in overcrowded camps and/or substandard housing, difficulties in adopting social distancing and self-isolation, and lack of adequate public health services. Using predetermined inclusion criteria for the studies, we searched databases, including JSTOR, Social Work Abstract, Social Sciences Abstract, EBSCOhost, ProQuest, and PsycINFO, to find relevant literature. We employed a theoretical construct of “uncanny,” often used by postcolonial thinkers, to critically analyze the selected studies. We identified four overarching themes: a) crisis within crises amid the pandemic, b) racism and xenophobia amid the pandemic, c) international solidarity and sharing responsibility, and d)neoliberal global regime and displacement. Our paper concludes with policy recommendations and action plans to be implemented by international communities, governments, and civil society targeting forcibly displaced people to mitigate the impacts of COVID-19 and future pandemics.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".