The modelling spiral for solving ‘wicked’ environmental problems: guidance for stakeholder involvement and collaborative model development
Bibliographic record
Abstract
Summary Many problems in environmental management and sustainability have no single, optimal solution. Such problems are called ‘wicked problems’. Any solution to a wicked problem will significantly affect a wide range of stakeholders, and cannot be separated from human ethics, values and social equity. Experience with participatory approaches that include stakeholders shows that ecological modelling can lead to applied outcomes that may inform environmental management and policy, thus helping to solve wicked problems. However, many ecological models fail to meet this goal. The key ways in which scientists and ecological modellers can contribute to the search for solutions to wicked problems in collaboration with stakeholders are described. Modelling is identified as a tool that scientists can bring into the deliberative process to facilitate dialogue and evidence‐based decision‐making within a stakeholder forum. A modelling spiral is proposed as a framework for describing the stages of the modelling process leading to positive change in collaboration with stakeholders. By following the recommendations of the modelling spiral, ecological modellers can accomplish socially relevant research that contributes to the collective search for sustainable solutions to wicked environmental problems.
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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 | 0.102 | 0.149 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".