Between regulatory field structuring and organizational roles: Intermediation in the field of sustainable urban development
Bibliographic record
Abstract
Abstract Recent contributions in the domains of governance and regulation elucidate the importance of rule‐intermediation (RI), the role that organizations adopt to bridge actors with regulatory or “rulemaking” roles and those with target or “rule‐taking” roles. Intermediation not only enables the diffusion and translation of regulatory norms, but also allows for the representation of different actors in policymaking arenas. While prior studies have explored the roles that such RIs adopt to facilitate their intermediation functions, we have yet to consider how field‐level structuring processes influence (and are influenced by) the various and changing roles adopted by RI. In this study we focus on the mutually constitutive relations between field‐level change processes and the evolving roles of RIs by studying the rise of the International Council for Local Environmental Initiatives (ICLEI)/Local Governments for Sustainability, an RI serving as a bridge for sustainable urban development policies between the United Nations and local authorities. Using ICLEI as an illustrative case, we theorize four different processes of regulatory field structuration: problematization, role specialization, marketization, and orchestrated decentralization. We discuss their implications for RI roles in the field and further theorize the changing dynamics of trickle‐up intermediation processes as an RI gains power and influence.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".