Mapping Our Cities for All as VGI Research: Completeness and Insights of a Crowdsourced Business Accessibility Dataset
Bibliographic record
Abstract
Volunteered Geographic Information (VGI) is a form of crowdsourcing which deals with spatial information. Given the spatial nature of accessibility barriers, the proliferation of crowdsourcing apps made by and for disabled people has provided easy-to-interpret repositories for business accessibility information. Claims made with VGI data are related to the dataset’s quality—such as positional accuracy, completeness, temporal accuracy, among others—and our research question answers the ‘completeness’ component of disability advocacy company AccessNow’s dataset. While previous work has theorized the potential of VGI for advancing civil rights or have investigated the utility of OpenStreetMap or Project Sidewalk as viable accessibility platforms, minimal work has applied data quality techniques to such data. Through the joint University of Calgary/AccessNow “Mapping Our Cities for All” (MOCA) initiative, 37 people were hired to map business districts in Vancouver, BC; Calgary, AB; Ottawa, ON; and 17 rural municipalities in Alberta. Using RStudio and ArcGIS Pro, we conducted completeness assessments for all study regions before exploring business accessibility through both spatial and industrial lenses. The findings of the MOCA project are being reported to Accessibility Standards Canada as a first attempt at quantifying our baseline level of accessibility, and which industries and regions could benefit from further investment, to work towards the goal of building a more accessible society.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".