Bibliographic record
Abstract
Abstract OpenStreetMap (OSM), an open source mapping platform aiming to provide a free world map by registered users, has often been recognized as one of the prime examples of volunteered geographic information (VGI) construction. This is reflective of the recent exponential growth of user‐generated geographic data facilitated by Web 2.0 technologies and location‐aware devices. Notable efforts have been taken to investigate OSM developments and associated socio‐political implications. However, still little is known about those less active contributors who constitute the majority of the contributors or long tail contributors in OSM and many other VGI initiatives. I therefore present an account of the dynamics of OSM mapping practices including these long tail contributors. Based on this investigation, I argue for a broader conceptualization of “interactivity” in VGI mappings, one that moves beyond a narrow focus on the mapping interface regarding the encounters between these VGI contributors and VGI initiatives. I suggest that this is helpful to better capture these dynamic and heterogeneous mapping practices constituting the data and representation in OSM, which in turn may have wider implications for everyday mapping and knowledge production.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".