Use of Participatory System Dynamics Modelling for Collaborative Watershed Management in Québec, Canada
Bibliographic record
Abstract
The participation of stakeholders is an important component in integrated and adaptive watershed planning and management. In Québec, 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 Québec. The application of the first three stages of a newly proposed five stage stepwise ‘Participatory Model Building’ framework’ is described that was developed to help facilitate the participatory investigation of problems in watershed planning and management through the use of the systems thinking approach (causal loop diagrams). In the Du Chêne watershed in Québec, 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 underlying the problem of water pollution. The method 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. The results of this study demonstrate the diversity of perspectives regarding the water quality problem in the Du Chêne watershed, and highlight the critical need for participatory approaches to help solve water quality problems. Based on the results generated in this study, stakeholders can jointly discuss diverging points of view, goals and interests by referring to a concrete system structure. This discussion process can again be facilitated by a group-built causal loop diagram and its subsequent conversion into a quantitative system dynamics model (describing the socioeconomic-political 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".