Bibliographic record
Abstract
Two concurrent 21st-century phenomena—the nearly unprecedented number of forced migrants and the near ubiquity of information and communications technology—have given rise to increased scholarship in “digital migration studies.” One area of investigation in this emergent interdisciplinary field is the role of digital skills in refugee integration. Given the accelerated global reliance on technology resulting from the COVID-19 pandemic, the author conducted a state-of-the-art literature review to identify emerging issues and highlight research needs in this area. A search of 10 databases yielded 39 studies spanning the major resettlement regions (North America, Western Europe, Oceania), and including refugees from across the globe. The inclusion criteria were studies focused on refugees’ practical use of digital technology in integration, published from January 2020-April 2021. Exclusion criteria were studies on refugees in transit or protracted displacement, digital connectivity and accessibility, use of digital technology by humanitarian actors, software development, analyses of digital representations of refugees, public attitudes toward refugees as expressed in digital media, and literature reviews. Ndofor-Tah et al.’s (2019) Refugee Integration Framework was used to organize and synthesize the findings. The studies demonstrated how digital skills affect all domains of integration. Additionally, the studies confirm that many refugees in resettlement have limited digital skills for necessary integration tasks, such as navigating websites and assessing the credibility of online information. Limitations of this state-of-the-art review include its cross-sectional nature, having only one reviewer, and only published literature accessible online through public websites or subscription databases. An important emerging issue for future research is assessing, teaching, and learning digital skills among this population. The study's contributions to the knowledge base and theory, and its implications for information science scholars and practitioners and those in allied disciplines within digital migration studies, are discussed.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".