Designing stakeholder learning dialogues for effective global governance
Bibliographic record
Abstract
Abstract A growing scholarship on multistakeholder learning dialogues suggests the importance of closely managing learning processes to help stakeholders anticipate which policies are likely to be effective. Much less work has focused on how to manage effective transnational multistakeholder learning dialogues, many of which aim to help address critical global environmental and social problems such as climate change or biodiversity loss. They face three central challenges. First, they rarely shape policies and behaviors directly, but work to ‘nudge’ or ‘tip the scales’ in domestic settings. Second, they run the risk of generating ‘compromise’ approaches incapable of ameliorating the original problem definition for which the dialogue was created. Third, they run the risk of being overly influenced, or captured, by powerful interests whose rationale for participating is to shift problem definitions or narrow instrument choices to those innocuous to their organizational or individual interests. Drawing on policy learning scholarship, we identify a six-stage learning process for anticipating effectiveness designed to minimize these risks while simultaneously fostering innovative approaches for meaningful and longlasting problem solving: Problem definition assessments; Problem framing; Developing coalition membership; Causal framework development; Scoping exercises; Knowledge institutionalization. We also identify six management techniques within each process for engaging transnational dialogues around problem solving. We show that doing so almost always requires anticipating multiple-step causal pathways through which influence of transnational and/or international actors and institutions might occur.
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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 | 0.061 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".