Inclusion by Design: Requirements Elicitation with Digitally Marginalized Communities
Bibliographic record
Abstract
A more equal and sustainable digital future depends on the inclusion of digitally marginalized communities in the socioeconomic opportunities created by digital technologies. Digital inclusion is a complex process that involves all stages of digital innovation, including development, adoption, use, and maintenance. However, past research has largely approached digital inclusion as an adoption and use challenge. In this paper, we develop a view of digital inclusion as a design challenge. We focus on the activities of requirements elicitation (RE) as a critical element of the design process and draw on a design-based interpretive study involving the design of two mobile apps for agricultural communities in India and China. We analyze how the conditions of digital inequality underlying the digital marginalization of these communities affect their sensemaking as they participate in RE activities. We conceptualize these challenges as limitations on the emergence of technology affordances. Our findings reveal various shifts, or translations, in the emerging affordances, which enabled the RE activities to be more generative and consequently more inclusive. These affordance translations manifested along three main dimensions: specificity, temporality, and collectivity. We discuss the implications of these findings for the inclusion of marginalized communities in the design of new technologies.
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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 | 0.042 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".