Participatory scenario development for environmental management: A methodological framework illustrated with experience from the UK uplands
Bibliographic record
Abstract
A methodological framework is proposed for participatory scenario development on the basis of evidence from the literature, and is tested and refined through the development of scenarios for the future of UK uplands. The paper uses a review of previous work to justify a framework based around the following steps: i) define context and establish whether there is a basis for stakeholder engagement in scenario development; ii) systematically identify and represent relevant stakeholders in the process; iii) define clear objectives for scenario development with stakeholders including spatial and temporal boundaries; iv) select relevant participatory methods for scenario development, during initial scenario construction, evaluation and to support decision-making based on scenarios; and v) integrate local and scientific knowledge throughout the process. The application of this framework in case study research suggests that participatory scenario development has the potential to: i) make scenarios more relevant to stakeholder needs and priorities; ii) extend the range of scenarios developed; iii) develop more detailed and precise scenarios through the integration of local and scientific knowledge; and iv) move beyond scenario development to facilitate adaptation to future change. It is argued that participatory scenario development can empower stakeholders and lead to more consistent and robust scenarios that can help people prepare more effectively for future change.
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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 | 0.066 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".