Modeling Collaborative GIS Processes Using Soft Systems Theory, UML and Object Oriented Design
Bibliographic record
Abstract
The evidence from collaborative GIS problem solving indicates that although environmental problems are context dependent, meaningful solutions are framed around the core issues of multiple stakeholder interests, complexity, wicked problems, ill-defined problem specification, and their spatial characteristics. Based on subsets of these issues, there exists a number of overlapping collaborative GIS designs and processes. The goal of this study is to reconcile the overlap by modeling a core collaborative GIS design and process. General systems theory is used to classify core technical components of the collaborative GIS design, and soft systems theory characterizes the human activity dynamics of the collaborative process. Further, object oriented principles are used to generate a flexible problem domain design, and the unified modeling language (UML) visually describes the structure and behavior of the collaborative process. The core formalism facilitates GIS process integration, standardization, reusability, ontology design, and rapid solution design in new problem contexts. The collaborative spatial Delphi (CSD) methodology is a proof of concept. This research contributes to the design and specification of a core collaborative GIS model, a reusable pattern, and their ontological impacts.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".