Collaborative GIS process modelling using the Delphi method, systems theory and the unified modelling language (UML)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,061 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,009 | 0,007 |
| Études des sciences et des technologies | 0,005 | 0,007 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».