Collaborative GIS process modelling using the Delphi method, systems theory and the unified modelling language (UML)
Bibliographic record
Abstract
Efforts to resolve environmental planning and decision-making conflicts usually focus on participant involvement, mutual understanding of the problem situation, evaluation criteria identification, data availability, and potential alternative solutions. However, as the alternatives become less distinct and participant values more diverse, intensified negotiations and more data are usually required for meaningful planning and decision-making. Consequently, questions such as "What collaborative spatial decision making design is best for a given context?" "How can the values and needs of stakeholders be integrated into the planning process?" and "How can we learn from decision making experiences and understanding of the past?" are crucial considerations. Answers to these questions can be developed around the analytic and discursive approaches that transform diffused subjective judgments into systematic consensus-oriented resolutions. This dissertation examines the above issues through the design, implementation, and assessment of the Collaborative Spatial Delphi (CSD) Methodology. The CSD methodology facilitates spatial thinking and discursive strategies to describe the complex social-technical dynamics associated with the knowledge-structuring-consensus nexus of the participation process. The CSD methodology describes this nexus by synthesizing research findings from knowledge management, focus group theory, systems theory, integrated assessment, visualization and exploratory analysis, and transformative learning all represented within a collaborative geographic information system (GIS) framework. The CSD methodology was implemented in multiple contexts. Its use in two contexts - strategic planning and management of urban green spaces in Montreal (Canada); and priority setting for North American biodiversity conservation - are reported in detail in this dissertation. The summative feedbacks from all the CSD planning workshops help incrementally improve the design of the CSD process. This dissertation also reports on the design and use of questionnaire surveys to incorporate local realities into planning, as well as the development of an evaluation index to assess the face validity and effectiveness of the CSD process from the perspective of workshop participants. The accumulated evidence from the CSD implementations suggests that many core issues exist across spatial problem solving situations. Thus, the design and specification of a core collaborative process model provides benefits for knowledge exchange. General systems theory was used to classify the core technical components of the collaborative GIS design, and soft systems theory was used to characterize the human activity dynamics. Object oriented principles enabled the generation of a flexible domain model, and the unified modelling language (UML) visually described the collaborative process. The CSD methodology is used as a proof of concept. This dissertation contributes to knowledge in the general areas of Geography, Geographic information systems and science, and Environmental decision making. The specific contributions are threefold. First, the CSD provides a synthesis of multi-disciplinary theories and a tested tool for environmental problem solving. Second, the CSD facilitates a fusion of local and technical knowledge for more realistic consensus planning outcomes. Third, an empirical-theoretical visual formalism of the CSD allows for process knowledge standardization and sharing across problem solving situations.
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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 | 0.061 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".