Visualizing stakeholders’ willingness for collective action in participatory scenario planning
Bibliographic record
Abstract
Participatory scenario planning is a powerful approach to guide diverse stakeholders in creating and reflecting on visions of plausible and desired futures. However, this process requires tools to guide collective action to implement such visions within management agendas. This study develops, applies, and analyzes a novel visual tool within a virtual participatory scenario planning process about the Sierra de Guadarrama National Park (Madrid, Spain). Building on the identification of stakeholders who might engage in scenario strategies, the visual tool guided them in defining tasks to be developed and envisioning their willingness to collaborate in their implementation. We qualitatively analyzed data from recordings, online field observations, a post-survey from the scenario planning process, and a successive policy workshop. Our findings show that the visual tool fosters dialogue between stakeholders to redistribute tasks for working together on needed strategies in the protected area while promoting reflection on their willingness to collaborate as a group to implement them. The visual tool provided graphic outcomes for nine strategies corresponding to pictures of who may or may not be willing to engage in implementing such strategies. We argue that the visual tool is a robust method that can complement participatory scenario planning processes by providing a useful starting point for creating action networks to incorporate the resulting scenario strategies into management agendas. We deliberate on the nature of the visual tool as a boundary object and discuss its role as a decision-support tool. In particular, we reflect on the potential contributions and limitations of the visual tool to four dimensions of participatory conservation governance during participatory scenario planning processes: inclusivity, integration, adaptation, and pluralism. Our study provides a practical orientation to adapt the tool to other contexts and knowledge co-creation processes.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".