Approaches to resolve conflict and support collaborative decision making in participatory transboundary watershed management
Bibliographic record
Abstract
Transboundary watersheds are often such large and complex systems that no one person can have complete knowledge of the hydrological, environmental, political, legal, economic, and social processes that interplay within them. With multiple jurisdictions using and maintaining the water system, no one individual or entity has the power to fully control how the system is governed. This means that when it comes to making decisions about how to manage watersheds, current best practice puts a lot of focus on collaborative processes that bring together a broad range of stakeholders with knowledge about distinct aspects of the watershedâs systems alongside those with different responsibilities and powers to govern it. Bringing together diverse stakeholders, who view the watershed from a range of perspectives, has the potential to lead to either conflict or cooperation. The challenge for transboundary water resources management is to find the means to bring together diverse perspectives in a way that fosters communication, and not confrontation. The aim of the present work is to contribute to addressing this challenge.The upper St Lawrence watershed is used as a case study to explore the themes of conflict and cooperation in transboundary watershed management. The study is broken down into two parts. The first looks to the recent past and presents a critical analysis of the stakeholder engagement and conflict resolution processes employed by the International Joint Commission during negotiations to change the management regime of the St Lawrence River. Audio recordings and transcriptions of public and technical hearings held by the IJC in 2013 were systematically analysed to assess the extent to which this process was able to achieve consensus in decision making, and understand the root causes of any residual conflict. The second part of the study looks to the future. Serious games have been touted as a novel tool with applicable value in supporting collaborative decision making. Seminal literature presenting a variety of distinctly different approaches to decision making was selected and reviewed to survey the breadth of decision making processes employed. Exploratory interviews were conducted with water resource managers across the St Lawrence region to explore whether a serious game might be useful in this particular context.A general conclusion is reached that both Shared Vision Planning and Serious Games increase the likelihood that, when diverse perspectives are brought together, collaboration will prevail over conflict. In the case of Shared Vision Planning, this is achieved by identification and resolution of disputed causal associations through collaborative model building; in the case of Serious Games, it is achieved by expanding the experience and awareness of actors by allowing them to role play another perspective. It is suggested that the Serious Games approach may have the advantage of helping develop empathy, as games can be designed to allow players to experience the challenges faced by other stakeholders first-hand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".