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Record W2787499810

An Inventory of Policy Actors and Instruments Relevant to the Salish Sea

2015· article· en· W2787499810 on OpenAlexfundaboutno aff
Stacy Clauson, Laurie Trautman

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersNatural Resources Conservation ServiceFarm Service AgencyCalifornia Department of Fish and WildlifeNational Park ServiceU.S. Forest ServiceU.S. Geological SurveyGovernment of CanadaNational Oceanic and Atmospheric AdministrationMinistry of EnvironmentNational Estuarine Research Reserve SystemWashington State UniversityU.S. Department of AgricultureNational Marine Fisheries ServiceU.S. Army Corps of EngineersU.S. Department of the InteriorU.S. Department of CommerceWashington State Department of AgricultureU.S. Department of DefenseTransport Canada
KeywordsPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

The Salish Sea Governance Study is a baseline inventory, designed to identify and categorize the variety of actors and instruments that bear on the maintenance and revitalization of the Salish Sea. Both sides of the border have regulations and actors working to mitigate the multitude of stressors adversely impacting the health of the Salish Sea and to preserve and restore the system. These include governmental entities at varying scales; indigenous communities working individually or integrating efforts; and non-state actors working within non-governmental organizations and NGO-networks. Formal and informal mechanisms bring these different actors together. Such interaction crosses multiple levels and orders of government and non-governmental civil society. This creates a complicated and, at times, fragmented approach to governance. This study aims to provide more clarity by creating a resource tool to improve awareness of the different governance systems (e.g. laws and policies and policy actors) affecting the Salish Sea in both the United States and Canada.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.661

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 science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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 teacher head, 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

Citations1
Published2015
Admission routes2
Has abstractyes

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