Quality Assessment of Volunteered Geographic Information: An Investigation into the Ottawa-Gatineau OpenStreetMap Database
Bibliographic record
Abstract
Within the realm of Volunteered Geographic Information (VGI), reliability and quality of the geographic information continues to be a pressing concern.Many VGI projects do not have standard geospatial data quality assurance procedures and the reliability of such contributors remains in question.This study investigates the quality of VGI by analysing OpenStreetMap (OSM) data in Ottawa-Gatineau.First, a review of past publications into quality assessment of OSM data is examined.Next, a comparative analysis of OSM data is conducted relative to an authoritative dataset.The OSM historical information of map features and contributors is inspected to gain an understanding of how users are contributing to the database and their ability to do so accurately.Overall, OSM data in the context of Ottawa-Gatineau is comparable to or surpasses authoritative dataset quality and clustering contributors based on historical information can help identify tendencies within a contributor base.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.026 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".