MétaCan
Menu
Back to cohort
Record W2155385112 · doi:10.3390/su1020254

Renegotiating the Great Lakes Water Quality Agreement: The Process for a Sustainable Outcome

2009· article· en· W2155385112 on OpenAlexaff
Gail Krantzberg

Bibliographic record

VenueSustainability · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSustainabilityNegotiationGovernment (linguistics)Quality (philosophy)Process (computing)MediationPolitical sciencePublic engagementOutcome (game theory)Environmental resource managementPublic administrationEnvironmental planningBusinessPublic relationsGeographyEconomicsLawEcologyComputer science

Abstract

fetched live from OpenAlex

This is a defining moment for the Great Lakes St Lawrence region, with the opportunity to renovate the regime for ecosystem improvement, protection and sustainability. The binational Great Lakes Water Quality Agreement was first signed in 1972. The outcome of a 2007 review of the Agreement by government and citizens, resulted in a broad call for and revisions to the Agreement, so that it can once again serve as a visionary document driving binational cooperation to address long-standing, new and emerging Great Lakes environmental issues in the 21st century. A prescription for renegotiating the Agreement to generate a revitalized and sustainable future mandates that science inform contemporary public policy, third Party Mediation presses for and coordinates a deliberate negotiation, and inclusive discourse and public engagement be integral through the process.

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)0.095
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.505

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.088
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.026
Scholarly communication0.0180.015
Open science0.0030.026
Research integrity0.0130.016
Insufficient payload (model declined to judge)0.0080.002

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 head0.031
GPT teacher head0.362
Teacher spread0.331 · 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 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

Citations7
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicWater Resources and GovernanceFrench-language works237,207