Corporate editors in OpenStreetMap: Investigating co‐editing patterns
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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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 | 0.010 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".