Participatory methods of integrated assessment—a review
Bibliographic record
Abstract
Abstract The field of Participatory Integrated Assessment (PIA) is still very young, having evolved from the broader field of Integrated Assessment (IA) in the mid to late 1990s. Like IA, PIA is a problem‐based field, with a focus on interdisciplinary research. Fundamental to PIA, however, is the assertion that the quality of decisions is improved by the direct involvement of stakeholders in the assessment process—particularly when those decisions pertain to complex, intractable problems. Climate change presents just such a problem, and it is in the domains of climate change and related sustainability issues where PIA has seen its broadest application. Previous reviews have focused primarily on the mechanisms of participation in PIAs. The purpose of this review is to take a broader look at the field of PIA, focusing on components and cross‐cutting themes that appear to be defining the conduct of PIA exercises. The review first looks at common components of PIA, including methods (future scenarios and models), participation (mechanisms of participation, representation, and stages of involvement), and outcomes (policy outcomes and process outcomes). The review then turns to an examination of cross‐cutting themes in the field of PIA. These themes include the tension between qualitative and quantitative information, the role of interactivity in PIA, the importance of institutions and institutional change, and navigating the space defined by choice, uncertainty, and constraints. As governments at all levels move toward response options for climate change, PIA is increasingly becoming an approach for providing meaningful participation in the selection of those options. © 2010 John Wiley & Sons, Ltd. This article is categorized under: Integrated Assessment of Climate Change > Participatory Methods of Integrated Assessment Social Status of Climate Change Knowledge > Climate Science and Decision Making
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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 | 0.038 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".