Lost in Translation: A Cross-Cultural Examination of Linguistic Inaccessibility in HCI
Bibliographic record
Abstract
This paper examines linguistic and cultural diversity in Human-Computer Interaction through multilingual experiences across various native languages, including Hungarian, Japanese, Cree, German, Welsh, Spanish, Mandarin, French, Polish, and Arabic. Each contribution reveals unique challenges in translation, usability, and cultural nuance within digital interfaces, with linguistic barriers ranging from issues with non-Latin characters to loss of contextual meaning and limited localisation options. These sections highlight the limitations of current design practices, which often prioritise English-centric frameworks that fail to accommodate diverse language structures and cultural nuances. By capturing these varied perspectives, this paper underscores the need for inclusive, cross-lingual design practices that address global usability challenges. It contributes to the development of more accessible and culturally sensitive digital environments, fostering an HCI approach that values linguistic diversity and cultural specificity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".