Learning to understand: disentangling the outcomes of stakeholder participation in climate change governance
Bibliographic record
Abstract
Stakeholder participation is increasingly seen as beneficial for short and long term responses to climate change risks. Past research highlights the role social networks play as both a key outcome of participation, as well as an important step towards other environmental governance goals. This paper focuses on the social relation of mutual understanding, which is often discussed in the environmental governance literature, but has yet to be studied as an empirical social network in its own right. Our paper builds and tests a conceptual framework linking participation to mutual understanding and social learning. We analyze three waves of network and perceptions data gathered on stakeholders participating in the Integrated Coastal Resiliency Assessment (ICRA) project, a 2.5 year-long project aimed at developing a collaborative research assessment on the vulnerabilities to climate change experienced by an island community located in the Chesapeake Bay, USA. Our findings suggest that participation (measured as co-attendance in project events) leads to the formation of mutual understanding ties among stakeholders, but these ties do not necessarily lead to more similarity in stakeholders’ perceptions on climate change. We reflect on these findings, and the project more broadly, noting that our study lends support to scholars arguing that feelings of mutual understanding are potentially more important for certain forms of collective action, as opposed to whether or not stakeholders increase their shared beliefs or perceptions about the environmental problem in question.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".