Networks in the Norm Life Cycle and the Diffusion of Environmental Norms
Bibliographic record
Abstract
In this research, I analyze how Transnational Municipal Networks (TMNs) and cities affect the diffusion and transmission of a decarbonization norm. Urban policy and political science scholars assert that cities and their networks are influential in implementing internationally coordinated environmental policy. However, few projects have analyzed how local actors may diffuse environmental norms that have been developed in the international system. Using the norm life cycle, this research explores the transmission of a decarbonization norm by means of GHG measurement and mitigation. I identify two critical objectives associated with a decarbonization norm: establishing a system for monitoring GHG emissions and developing action plans to reduce emissions. These two components of decarbonization are also viewed as indicators of norm leadership, which applies to all levels of governance. The International Council for Local Environmental Initiatives (ICLEI) is a transnational municipal network (TMN) that facilitates networking between cities, communication between the transnational and local levels, and the development of local responses to climate change. To analyze the influence of ICLEI on the local environmental policy of cities, I used a logistic regression analysis to explore three time periods (1991-2002, 2003-2010, and 2011-2018). The findings of the analysis support my hypothesis that both cities and their networks play a significant role in the diffusion of a decarbonization norm. TMNs, like ICLEI, supply technical assistance that guides policy and provides a platform for local leaders to act transnationally. Cities are more likely to adopt decarbonization objectives if they obtain ICLEI services or membership. Furthermore, through the results of my analysis, I provide evidence that cities in the United States and Canada acted first as norm entrepreneurs, and then as norm leaders by creating systems for monitoring and mitigating GHG emissions.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".