Design for the Developing World: Common Pitfalls and How to Avoid Them
Bibliographic record
Abstract
Engineers face many challenges when designing for the developing world, which are not typically encountered in other design circumstances, such as a lack of understanding of language, culture, and context. These challenges often prevent engineers from having a sustained impact as they design for resource-poor individuals. In this paper, reports from 41 engineering projects in the developing world were analyzed, and common pitfalls were identified. The data came from Failure Reports from Engineers Without Borders (EWB) Canada and from the authors' own field reports. After the pitfalls are described, the authors present a visual tool called the Design for the Developing World Canvas to help design teams that are developing manufactured products to avoid these common pitfalls. This canvas can be used throughout the product development process as part of regular design reviews to help the team evaluate their progress in advancing the design while avoiding the pitfalls that engineers commonly face.
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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 | 0.055 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".