Geographic scale dependency and the structure of climate adaptation policy networks in San Francisco Bay
Bibliographic record
Abstract
Research on collaborative governance, polycentric governance, and policy networks shares the hypothesis that policy networks emerge to solve collective-action problems across multiple levels of geographic scale. Policy networks provide social capital in the form of information and trust-based relationships, which enable the involved actors to learn and cooperate to address environmental risks. We argue that policy networks in polycentric governance systems are scale dependent in both structure and function. The structure of policy networks varies across levels of geographic scale, with regional-level networks presenting more structural features that support learning and cooperation. Also, local networks are more responsive to the varying risks of sea-level rise in different localities. As policy networks scale up to higher levels of geographic scale, network structures become more homogenous, driven by the regional actors’ concern for the well-being of entire regions. Drawing from a stakeholder survey in the context of sea-level rise and climate adaptation networks in San Francisco Bay, we define networks at multiple geographic scale based on the level of policy actors’ engagement with local coastal planning units. Our social network analysis findings underscore that regional actors are crucial sources of social capital for solving climate adaptation collective-action problems and that sea-level rise vulnerability is especially associated with the emergence of bonding social capital. Environmental risk, such as sea-level rise, will urge the need for collective actions across geographic scales, and our studies suggest that regional actors can provide public good across regions and reduce the transaction costs of building policy networks between disadvantaged communities.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".