Bibliographic record
Abstract
examines citizens ’ preferences for assigning policy responsibility for environmental problems to different levels of government. I find that the public generally prefers the federal government to take the lead in addressing most issues, particularly those that relate to pollution and those that have a national or global scale.The public, however, prefers to give more responsibility to state and local governments to handle local-level issues. These results suggest a desire among many in the public to match governmental policy assignment with the geographic scale of the problem. The best predictor of individual’s choice of government level is political orientation, and to a lesser extent one’s general confidence in each level of government. Should the responsibility to address environmental problems rest with the national or subnational levels of government? This question has been at the center of a long-standing debate about the institutional design of environmental policy in the United States (Anderson and Hill 1997; Esty 1996; Kraft and Scheberle 1998), and in other federal systems of government, such as Canada (Harrison 1996) and Germany (Rose-Ackerman 1995). For much of the past half century, U.S. envi-ronmental policy can, in part, be characterized, as a tug-of-war between federal,
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".