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Record W4404198965 · doi:10.1080/1523908x.2024.2426171

‘Green icing on the Gray Cake?’ Green infrastructure in federal enforcement action

2024· article· en· W4404198965 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Environmental Policy & Planning · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEnforcementGreen infrastructureBusinessPublic administrationEquity (law)Federal lawPolitical scienceLegislationEconomicsEnvironmental resource managementLaw

Abstract

fetched live from OpenAlex

A growing number of local governments are adopting Green Infrastructure (GI) as a strategy for urban stormwater management. In the United States, GI has gained particular momentum through federal initiatives. Previous studies have identified regulatory factors playing a pivotal role in the adoption of GI at the local level. However, the integration of GI with federal enforcement actions has received limited research attention. This study examines the first generation of federal consent decrees with GI provisions to provide insights into federal GI policy and local GI planning. The findings reveal inconsistency in GI requirements, with some cases merely encouraging GI adoption, while others mandating a more specific approach to planning and implementation. Drawing on existing GI scholarship, the results of this study suggest the establishment of universal requirements, such as piloting projects, defining success criteria, and incorporating equity considerations into local GI planning.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.268
Teacher spread0.248 · 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