MétaCan
Menu
Back to cohort
Record W1489753409 · doi:10.5751/es-01359-100135

Incorporating Science into the Environmental Policy Process: a Case Study from Washington State

2005· article· en· W1489753409 on OpenAlexvenueno aff
Tessa B. Francis, Kara A. Whittaker, Vivek Shandas, April Mills, Jessica K. Graybill

Bibliographic record

VenueEcology and Society · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsnot available
FundersUniversity of WashingtonNational Science Foundation
KeywordsState (computer science)Environmental policyProcess (computing)Environmental ethicsPolitical scienceEcologyEnvironmental resource managementEnvironmental scienceComputer sciencePhilosophyBiology

Abstract

fetched live from OpenAlex

Francis, T., K. Whittaker, V. Shandas, A. V. Mills, and J. K. Graybill. 2005. Incorporating science into the environmental policy process: a case study from Washington State. Ecology and Society 10(1):35. https://doi.org/10.5751/ES-01359-100135

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0200.005
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0050.005
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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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.

Study designQualitative
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

Citations43
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueEcology and SocietySame topicSustainability and Climate Change GovernanceFrench-language works237,207