Legal Issues in Maps Built on Third Party Base Layers
Bibliographic record
Abstract
The recent growth in citizen map-making ability has been brought about in part by the availability of base layers of geospatial information on which maps can be built, as well as software tools that allow geographic information to be represented. However, the legal relationship between the creator of the map and the owner of the base layer has received relatively little attention. In this paper, we consider legal issues regarding volunteered geographic information (VGI) submitted to third-party geographic information systems (GIS). This combination raises issues of copyright, database rights, trademark, and End User License Agreements (EULAS). The paper will consider the IP rights on which the EULAs are founded and the corresponding rights of those who build their own maps onto the base layers; analyze some of the key EULAs in this area, and identify important issues for those who create maps using these base layers.
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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 | 0.060 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| 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".