Disruptive Innovation: The Lasting Outcomes of Participatory Mapping and Volunteered Geographic Information in Nepal’s Post-Earthquake Recovery
Bibliographic record
Abstract
Abstract. Volunteered Geographic Information (VGI) is spatial data collected through digital participatory mapping, where non-expert volunteers create and share spatial knowledge. Despite claims of empowerment and improved decision-making, the long-term outcomes of participatory mapping remain under-evaluated. In humanitarian emergency response, VGI production enables the creation of updated real-time post-crisis maps, thus improving response effectiveness. This paper examines the lasting socio-economic outcomes of VGI production in humanitarian assistance, beyond the immediate crisis period, with a focus on the 2015 Nepal earthquake response. VGI production led to the creation of the first freely available and comprehensive digital basemap for Nepal, which catalyzed the development of new spatial services and platforms. Beyond disaster relief, the mapping data provided a foundation for new business and educational opportunities, including Baato Maps, a culturally relevant, low-cost navigation tool. The adoption of these tools has fostered a competitive ecosystem for spatial services like ride-sharing, e-commerce, and delivery, fostering economic resilience. This paper demonstrates how participatory mapping, when purposefully integrated, can drive disruptive innovation and create socio-economic benefits which support the transition from emergency response to long-term development. It highlights the importance of incorporating community generated VGI into future humanitarian planning and evaluation to support sustainable, community-driven innovation and enhance long-term resilience.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".