IslamicHCI: Designing with and within Muslim Populations
Bibliographic record
Abstract
In recent years there has been a growing body of work from the CHI communities that looks at designing for inclusivity and for the unique and specific constraints of diverse populations. This has included but is not limited to, work on designing within patriarchal contexts, designing around issues of gender and sexual orientation and designing around literacy. In tandem, local HCI initiatives such as ArabHCI [3] have emerged to address the misrepresentation of these populations in HCI research, highlighting the fact that Western originated design methods would require delicate adaptations to suit non-Western cultural contexts. With the same approach towards inclusivity and co-existence the aim of this workshop is to bring together HCI researchers and practitioners who engage in studies and interventions within Muslim majority communities around the world. The goal is to understand the Muslim identity and perceptions around it, the unique constraints and limitations within Muslim communities and to identify core issues and concerns within these populations. We will explore the following themes: refugees and islamophobia; Muslim feminism and Digital financial services.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".