Stakeholder engagement with environmental decision support systems: The perspective of end users
Bibliographic record
Abstract
Environmental decision support systems (EDSS) are designed to assist natural resource managers and stakeholders to assess problems and select options for change. EDSS that combine community engagement in developing future scenarios with computer‐based land use planning and modelling tools are widely used internationally. However, these EDSS are often not used after the research and development phase. To best understand why the EDSS are not being used in the long term, the end users of the EDSS should be consulted—a perspective that is lacking in the literature. The research reported here presents the perspectives of stakeholders involved in a community climate change adaptation project in western Canada. Evidence from the community suggests that this project was successful in instigating change. However, the EDSS was not used after the project's end. Our findings indicate that, from the end users’ perspective, the project could have had much greater and sustained success had there been ongoing engagement and communication with them, particularly in the form of continued support for the use of EDSS after the development project.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".