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Record W654306318

Green Leviathan: The Case for a Federal Role in Environmental Policy

2009· book· en· W654306318 on OpenAlexaboutno aff
Inger Weibust

Bibliographic record

VenueMedical Entomology and Zoology · 2009
Typebook
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsFederalismCorporate governanceEnvironmental governanceEnvironmental policyGovernment (linguistics)Political scienceDevolution (biology)Public administrationMulti-level governanceEnvironmental planningEconomicsEnvironmental resource managementSociologyPoliticsGeographyLawManagement
DOInot available

Abstract

fetched live from OpenAlex

The US, Switzerland and Canada are wealthy democracies that should be conducive to effective decentralized or cooperative environmental policy-making. However, a closer examination of their environmental policy over many decades finds no evidence that these approaches have worked. So does it matter which level of government makes policy? Can cooperation between sub-national governments protect the environment? Building on comparative case studies on air and water pollution and making use of extensive historical material, Inger Weibust questions how governance structure affects environmental policy performance in the US, Switzerland, Canada and the European Union. The research breaks new ground by studying formal and informal environmental cooperation. It analyzes whether federal systems with more centralized policy-making produce stricter environmental policies and debates whether devolution and the establishment of subsidiaries will lead to less environmental protection. An essential insight into the complexities of policy-making and governance structures, this book is an important contribution to the growing debates surrounding comparative federalism and multi-level governance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.037
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.012
GPT teacher head0.291
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations43
Published2009
Admission routes1
Has abstractyes

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