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Record W2529674451 · doi:10.1177/0160323x16671004

Public and Local Government Leader Opinions on Environmental Federalism

2016· article· en· W2529674451 on OpenAlexaffabout
Sarah Mills, Christopher Gore

Bibliographic record

VenueState and Local Government Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFederalismLocal governmentContext (archaeology)Public administrationGovernment (linguistics)Public opinionWork (physics)Political scienceNew FederalismNational governmentPoliticsLawGeography

Abstract

fetched live from OpenAlex

How should regulatory authority over environmental policy issues be allocated? While there has been previous work on public preferences regarding environmental federalism, less attention has been paid to the opinions of local government officials, who are often on the front lines testing the limits (or not) of their authority. Using survey data of public and local official opinion about environmental federalism, this article finds that local government officials often see a significantly different role for national, subnational, and local government than their constituents, even after accounting for demographic differences between the groups. This article draws on data from two subnational jurisdictions in different countries (Michigan, United States and Ontario, Canada) and finds that the differences between the general public and local officials are durable even with a change in national context.

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)0.031
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.291
Teacher spread0.249 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2016
Admission routes2
Has abstractyes

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