uMap: A Free Open-Source Alternative to Google My Maps
Bibliographic record
Abstract
Since their release in 2005, Google Maps-based tools have become the de facto solutions for a variety of online cartographic projects. Their success has been accompanied by a range of critiques denouncing the individualistic market-based logic imposed by these mapping services. Alternative options to this dominant model have been released since then; uMap is one of them. uMap is a free, open-source online mapping platform that builds on OpenStreetMap to enable anyone to easily publish web maps individually or collaboratively. In this paper, we propose to reflect on the potential and limits of uMap based on our own experiences of deploying it in six different mapping projects. Through these experiences, uMap appears particularly well-suited for collaborative mapping projects, due to its ease in connecting to remote data and its high level of interoperability with a range of other applications. On the other hand, uMap seems less relevant for crowdmapping projects, due to its lack of built-in options to manage and control public contributions. Finally, the open-source philosophy of uMap, combined with its simplicity of use and its strong collaborative capacity, make it a great option for activist mapping projects as well as for pedagogical purposes to teach a range of topics including online collaborative cartography.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.048 |
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".