Bibliographic record
Abstract
Abstract Geospatial technologies implemented through the World Wide Web (Geoweb) have improved steadily over the last decade. This Geoweb has the potential to allow citizens to collect and use geospatial data in an effort to influence urban planning in a bottom‐up manner—a stark departure from traditional top‐down public consultation in urban planning. This article reviews five classes of geospatial data available to citizens trying to influence urban planning. These classes have been reviewed in the academic literature before, but not at the same time and not with respect to urban planning. Looking at them together allows for examination of the interconnection as well as the boundaries between them. This paper establishes an understanding of these Geoweb data classes and examines the main barriers citizens face when trying to use geospatial data—with a focus on technological and financial barriers. Despite improvements in the Geoweb that help in reducing these barriers, preliminary evidence suggests that the voices of citizens are still not being fully heard in urban planning. This article tells the story of a Geoweb project by Paths for People, a biking and walking advocacy group in Edmonton, Canada. Paths for People initiated a Public Participatory Geographic Information Systems (PPGIS) project to inform the development of biking infrastructure in Edmonton, but this was largely ignored by city council, which adopted a top‐down cycling plan. This paper highlights the large gap between the promises of the Geoweb, the current practice of urban planning, and grassroots capabilities of this form of community engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".