Disparity, Instability, and Power in the Crowdmapping Ecosystem
Bibliographic record
Abstract
Crowdmapping is part of an evolution in participatory mapping, which shifted to the Participatory Geographic Information Systems of stand-alone offline software packages, and now embraces numerous online technologies. In community informatics, we focus on the need to sustain these systems, which supposedly has been dramatically eased with the introduction of online mapping tools and has democratized the technology. The literature is largely absent of the ways in which crowdmapping exists in an ecosystem of private sector and nonprofit actors, operating in an arena of non-human artifacts, such as hardware, software, and data. We reflect on five community-based crowdmapping applications (apps). All apps were in Canada (Montreal and Vancouver) with goals of fighting densification, highlighting lack of affordable housing and family-oriented greenspaces, promoting community assets, increasing findability of healthy food sources, and collecting perceptions of university spaces. We utilized a design ethnography to identify components of our ecosystem and actor-network theory to map the ecosystem. Our findings reveal the crowdmapping ecosystem (1) faced several interoperability challenges for technical implementation, which brought into sharp relief the disparate skill levels and resource capacities of developer and communities as well as the ability to respond to almost daily modifications in hardware, software, and data; (2) relied on an ever-shifting network of individuals and organizations in large part because of unsustainable business models serving a top-down governance; and (3) exposed power differentials among a mix of funders, tech-for-good nonprofits, private sector hardware and software providers, and the underlying non-human actors. Lessons learned from this ecosystem inform crowdmapping as it evolves and engages newer actors and technologies, which further inform community-based organizations as well as researchers and philanthropic funders who may promote overly complex solutions to suit particular agendas.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".