Corporate editors in OpenStreetMap: Investigating co‐editing patterns
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
Abstract Traditionally, OpenStreetMap (OSM) has been recognized as a volunteered geographic information (VGI) project. In recent years, many corporations have enlisted teams of mappers to edit data on OSM. These teams of corporate editors (CEs) can quickly edit large swaths of data using a variety of methods. Consequently, there are new tensions over possible community bifurcations where editing and map stewardship disagreements may occur between the CEs and non‐CEs. To characterize CE and non‐CE editing interactions, we focused on six locations with varied types of corporate editing activity. We created six temporal (2015–2020) editing networks for each location, resulting in 36 total networks. We found a continual increase in the number of editors, with more growth in places with CEs. There was significant co‐editing between the two groups, with CEs showing more in‐group editing patterns, both in terms of number of edits and time between edits. We conclude that currently the CE and the non‐CE communities continue to co‐exist and co‐produce open geospatial data in apparent harmony, even though the size of the CE community and volume of contributions have grown significantly. Finally, we discuss implications for OSM as a VGI project in light of our corporate editing trends.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it