Mapping Our Cities for All as VGI Research: Completeness and Insights of a Crowdsourced Business Accessibility Dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it