Bibliographic record
Abstract
The emergence of Web 2.0 is materialized by new technologies (APIs, Ajax, etc.), by new practices (mashup, geotagging, etc.) an, by new tools (wiki, blog, etc.). It is primarily based on the principle of participation and collaboration. In this dynamic, the web mapping with spatial character or simply called Geospatial Web (or Geoweb) evolves by strong technological and social changes. Participatory GeoWeb 2.0 is materialized in particular by mashups among wikis and géobrowsers (ArgooMap, Geowiki, WikiMapia, etc.). The new applications resulting from these mashups are moving towards more interactive forms of collective intelligence. The Geodesign is a new area, which is the coupling between GIS and design, allowing a multidisciplinary team to work together. As it is an emergent term, the Geodesign has not be well defined and it requires innovative theoretical basis, new tools, media, technologies and practices to fit its complex requirements. In this document, we propose some GeoWeb 2.0 tools and technologies that could support the Geodesign process. The main contributions of the present research are firstly identifying the needs, requirements and constraints of Geodesign process as an emergent fuzzy field, and secondly offering new supports that are best meeting to the collaborative dimension of this process.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".