Technology, Power, and Social Inclusion: Afghan Refugee Women's Interaction with ICT in Germany
Bibliographic record
Abstract
Afghan refugee women settle in Germany to escape persecution by militant groups and social marginalization in Afghanistan, among other things. They face challenges in Germany, such as language barriers, demanding bureaucratic requirements from German administrations, and discrimination. Academic and public discourses promote the information and communication technologies (ICT)-enabled social inclusion of refugees into the host society. ICT is widely seen as an essential tool to support refugees. Against this backdrop, this paper presents a focus group study with 14 Afghan refugee women in Germany to understand their experiences with technology: How do Afghan refugee women in Germany experience ICT? What structural factors influence their interaction with technology? What are the design features in an application that can support their settlement in Germany? This paper uses a critical perspective inspired by Black feminist theory to foreground the dynamics of power in Afghan refugee women’s experiences with ICT. The analysis reveals significant barriers to the participation of Afghan refugee women in German digital society, like digital illiteracy and the need for safety and privacy, making accessing technology difficult. Designs of ICT that may benefit Afghan women offer audio messages instead of text, real-time assistance, intuitive commands, and registration without an email address. Apart from the analysis of Afghan refugee women’s interaction with technology in German society, this paper reflects on the German migration management infrastructure and its potential to adapt more to the communication practices of refugees, including offering in-person services for Afghan refugee women.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| 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".