What Drives Norm Success? Evidence from Anti–Fossil Fuel Campaigns
Bibliographic record
Abstract
Why do some international norms succeed, whereas others fail? We argue that norm campaigns are more likely to succeed when the actions they prescribe are framed as a solution to salient problems that potential adopters face, even if different from the problem that originally motivated norm entrepreneurs. For instance, the campaign to reduce environmentally harmful fossil fuel subsidies has been more effective when linked to fiscal stability, a common problem that policy makers face. Problem linkages can thus bolster the attractiveness of a proposed new norm and broaden the coalition of actors that support the norm. We probe the plausibility of this argument by studying two campaigns that aim to shift patterns of finance for fossil fuel production and consumption: subsidy reform and divestment. Subsidy reform encourages governments to reduce subsidies for products like gasoline; divestment encourages investors to sell or avoid equity stocks from fossil fuel industries. We look at the variation in the impact of these two campaigns over time and argue that they have achieved institutional acceptance and implementation chiefly when their advocates have been able to link environmental goals with other goals, usually economic ones.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".