“Power to the people”: Contesting urban poverty and power inequities through open GIS
Bibliographic record
Abstract
Geospatial technologies are central to spatial decision making and governance, but gaining equitable access to these is still difficult for traditionally marginalized communities. We contend that the dominance of proprietary GIS software has contributed to this digital divide, as these are inherently disempowering to marginalized social groups. Their high purchasing cost and licensing fees pose access barriers to resource‐poor citizens. Design of proprietary software may also not be appropriate for marginalized groups who are neither trained in GIS, nor represent the needs of dominant market base. Therefore, “free and open source software for geospatial” (FOSS4G) and open GIS provide new opportunities in democratizing GIS, as these are open code and free of purchasing and licensing costs. This paper aims to discuss the role of open GIS in advancing the goals of public participation GIS (PPGIS). We first discuss the origins of the FOSS movement, and explore the ways it has shaped the FOSS4G and open GIS movements. Next, we examine how a community information system built with open GIS software is being successfully utilized by an environmental organization in Milwaukee, to contest urban poverty. Our research demonstrates that open source GIS offers unique opportunities in advancing PPGIS research and practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".