Achieving Effectiveness in Stakeholder Participation Using the GIS-Based Collaborative Spatial Delphi Methodology
Bibliographic record
Abstract
Environmental problems have spatial origins and consequences. Hence, effective stakeholder participation demands the integration of comprehensive facilities for understanding the spatial components that affect environmental change. This study develops the Collaborative Spatial Delphi (CSD) methodology to embed spatial thinking, conceptualise the dynamic mechanisms, and assess the effectiveness of the resulting participatory process. The CSD uses a collaborative geographic information system (GIS) and the Delphi procedure in a descriptive decision-making framework that integrates diverse stakeholder knowledge for spatial awareness, understanding, and negotiated outcomes. The CSD synthesises relevant theories to conceptualise the participation mechanisms, and an application of the methodology to urban green spaces planning in Montreal, Canada is presented. The results suggest that a deficiency in technical background is not a barrier to effective use of spatial technology in participatory planning. The methodology enhanced many spatial facets of the participation process and was evaluated as effective in achieving negotiated outcomes.
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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 | 0.127 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".