Participatory water resources planning and management in an Agriculturally Intensive Watershed in Quebec, Canada using Stakeholder Built System Dynamics Models
Bibliographic record
Abstract
Participatory water resources planning and management in an Agriculturally Intensive Watershed in Quebec, Canada using Stakeholder Built System Dynamics Models The participation of stakeholders is an important component in integrated and adaptive watershed planning and management. In Quebec, Canada watershed organizations are in the process of implementing participatory based watershed planning and management schemes. However, there is a lack of simple and readily implementable frameworks and methods to explicitly involve stakeholders, as well as integrate physical and social processes, in watershed planning and management in Quebec. This paper describes the application of the first three stages of a newly proposed five stage stepwise Participatory Model Building framework that was developed to help facilitate the participatory investigation of problems in watershed planning and management through the use of qualitative system dynamics models. In the agriculturally intensive Du Chene watershed in Quebec, eight individual stakeholder interviews were conducted in cooperation with the local watershed organization to develop qualitative system dynamics models that represent the main physical and social processes in the Du Chene watershed. The proposed Participatory Model Building framework was found to be accessible for all the interviewees, and was deemed to be very useful by the watershed organization to develop an overview of the different perspectives of the main stakeholders in the watershed, as well as to help develop watershed policies and strategies. The individual qualitative system dynamics models developed in this study can subsequently be converted into an overall group built system dynamics model (describing the socio-economic-political components of the watershed), which in turn can be quantified and coupled with a physically based model such as HEC-HMS or SWAT (describing the physical components of the watershed).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".