A Geolinguistic Approach for Comprehending Local Influence in OpenStreetMap
Bibliographic record
Abstract
OpenStreetMap (OSM) thrives on allowing anyone in the world to contribute features to a free online geographical database, thereby allowing international mixes of contributors to create the map in any given place. Using South America as a test area, I explore the geography of OSM contributors by applying automated language identification to the free-form comments that contributors make when saving their work. By cross-referencing these languages with users' self-reported hometowns from their profiles, I evaluate the effectiveness of language detection as a method for inferring the percentage of local contributors versus the percentage of “armchair mappers” from elsewhere. I show that most English-speaking contributors to the South American OSM are from outside the continent (rather than multilingual locals). The percentage of English use is higher in poor areas and rural areas, suggesting that residents of these places exercise less control over their map contents. Finally, I demonstrate that some features related to daily needs of health, education, and transportation are mapped with higher priority by contributors who speak the local language. These findings give researchers and organizations a deeper understanding of the OSM contributor base and potential shortcomings that might affect the data's fitness for use in any given place.
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.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".