Collaborative Geographic Information Systems and Science
Bibliographic record
Abstract
The relevant research literatures, together with the issues articulated by chapter authors in this book, are used to characterize some of the conceptual and technical hurdles of collaborative GIS. The intention is to examine how collaborative GIS can consolidate and expand its reaches in research and applications. This is necessary, given the increasing complexity of environmental and societal problems. Three areas for future research explorations are synthesized. These areas encompass scale effects, system modeling, and distributive planning. While there may be other equally valid research areas, the focus is to encourage a transdisciplinary infusion to enrich collaborative GIS. With a transdisciplinary approach, integrated solutions can be developed that use the best available knowledge to narrow conceptual gaps between technical experts and the general public, towards more effective planning, problem solving, and decision-making. Game theory, ontological engineering, and agent technology are proposed as transdisciplinary means to enrich current collaborative GIS research and applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".