Learning to understand: disentangling the outcomes of stakeholder participation in climate change governance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it