Disciplined Planning, Structured Participation, and Collaborative Modeling — Applying Shared Vision Planning to Water Resources
Bibliographic record
Abstract
Abstract Participatory planning applied to water resources has sparked significant interest and debate during the last decade. Recognition that models play a significant role in the formulation and implementation of design and management strategies has encouraged the profession to consider how such models can be best implemented. Shared Vision Planning (SVP) is a disciplined planning approach that combines traditional water resources planning methodologies with innovations such as structured public participation and the use of collaborative modeling, resulting in a more complete understanding and an integrative decision support tool. This study reviews these three basic components of SVP and explains how they are incorporated into a unified planning approach. The successful application of SVP is explored in three studies involving planning challenges: the National Drought Study, the Lake Ontario‐St. Lawrence River Study, and the Apalachicola‐Chattahoochee‐Flint/Alabama‐Coosa‐Tallapoosa River Basin Study. The article concludes by summarizing the advantages and limitations of this planning approach.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".