The Challenges of Integrating Texting and Mapping for Community Development in Canada
Bibliographic record
Abstract
Online mapping platforms allow non-experts to visualize, organize and create a dialogue of events. Ushahidi is the best-known example of a mapping platform used for critical events and natural disasters. The Ushahidi developers have integrated mobile technologies into their mapping by allowing for texting (SMS) to collect citizen reports. We used Crowdmap, Ushahidi’s cloud-based mapping platform to investigate whether citizens could “text community development”: to contribute SMSs in a meaningful way about community assets (e.g., eateries, parks, and schools). Youth was targeted because it was hoped that comfort with mobile devices may increase engagement of underrepresented age cohorts in an inner-city neighbourhood. We customized the platform to develop an application for a community development initiative in the neighbourhood of Lachine, within Montreal, Canada. This paper discusses technical components and challenges of a coupled Crowdmap-SMS deployment in Canada. We found challenges related to the nature of Canadian mobile network providers and mobile modem locks, interoperability of mapping and telecom software, parsing of locations, issues of content moderation and anonymity, and long-standing sustainability of diffusing ICTs to community based organizations. By focusing on community development, we sought to question the utility of these crisis-driven platforms towards persistent community based conditions. To counter certain challenges, we employed creative methods like storyboarding for communicating with both non-technical and multilingual audiences. The resulting application provides compelling visualization of maps and user statistics, although cartographic comprehension varied by user. For an automated system, the application required substantial manual interventions for day-to-day operations. In some cases, community members found the automatic mapping component more onerous than the texting. ICTs and open source software combine to create innovative possibilities for community development practices and self-organization, and to spark neighbourhood dialogues about local issues. We conclude by discussing broader implications and prospects of mobile-enabled mapping for community 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".