Opaque Obstacles: The Role of Stigma, Rumor, and Superstition in Limiting Women’s Access to Computing in Rural Bangladesh
Bibliographic record
Abstract
Marginalized communities’ access to and use of ICT have long been a concern in HCI4D and social computing. Many works in this domain have pointed out that the challenges to access to ICT often go beyond limited computing resources and skills and frequently include many other socio-cultural factors. In this paper, we report three of the factors that arose while studying rural Bangladeshi women’s access to ICT: stigma, rumor, and superstition. Through an eight-month-long mix-method study with 23 rural women in Jessore, we explored the forms of fear and resistance to use computing devices prevalent among this population, particularly among the women we studied. We report how their stigma, rumors and superstitions often entangled with each other and created a gender-specific resistance to women’s ICT use. This paper further discusses how this resistance was connected to a weak economy and insufficient legal and educational infrastructure in the rural community. We extend the discussion to implications for design, policy, patriarchy, and other social practices to address these human factors in HCI4D and social sustainability scholarship.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".