Towards Sustainable ICTD in Bangladesh: Understanding the Program and Policy Landscape and Its Implications for CSCW and HCI
Bibliographic record
Abstract
Historically, Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction for Development (HCI4D) researchers in the Global South have advocated for a community-based approach to technology design and development. However, even with this "bottom-up" emphasis, the sustainability and scalability of the resulting innovations remain major challenges, and are poorly understood. To address this gap, we take the case of Bangladesh as a typical Global South context in which development work is carried out by a complex intertwined network of stakeholders across governments, NGOs, donors, and industries. To better understand the current development landscape and its priorities for digital technologies, we conducted interviews with 14 influential decision-makers in Bangladesh who play significant roles in the development of nutrition strategies. Our findings highlight a disconnect between the Bangladesh government's "digital mandate" and the reality of digital innovation practice within the nutrition development sector. Our paper contributes to the debate on factors that affect decision-making processes. We explore the dynamics of diverse actors and institutions who are intended to participate in, but can act as obstacles to sustained bottom-up innovations. Our findings expand understanding of institutional priorities, the dynamics of intermediaries, techno-solutionism, postcolonialism, bureaucracy, competition, and other important topics in CSCW scholarship. We suggest understanding the factors that guide the decision-making process of digital innovation practices in terms of four dimensions: internal, external, vertical, and horizontal. Consequently, we recommend CSCW and HCI researchers become mediators to connect decision-makers and communities and bring their voices in ICT innovations for global development. Finally, we offer recommendations for proactive engagement with decision-making stakeholders, enabling researchers to design community-centered sustainable digital innovations for development.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".