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Record W2966140729 · doi:10.1016/j.cosust.2019.04.004

The Earth System Governance Project as a network organization: a critical assessment after ten years

2019· article· en· W2966140729 on OpenAlexaff
Frank Biermann, Michele M. Betsill, Sarah Burch, John S. Dryzek, Christopher Gordon, Aarti Gupta, Joyeeta Gupta, Cristina Yumie Aoki Inoue, Agni Kalfagianni, Norichika Kanie, Lennart Olsson, Åsa Persson, Heike Schroeder, Michelle Scobie

Bibliographic record

VenueCurrent Opinion in Environmental Sustainability · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Waterloo
FundersLunds UniversitetUnited States-Israel Binational Science FoundationUniversiteit UtrechtCenter for Outcomes Research and Evaluation, Yale School of Medicine
KeywordsCorporate governanceSustainabilityEarth system scienceSocial network analysisPolitical scienceField (mathematics)Public relationsEnvironmental resource managementSociologyManagementEcologySocial mediaEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

The social sciences have engaged since the late 1980s in international collaborative programmes to study questions of sustainability and global change. This article offers an in-depth analysis of the largest long-standing social-science network in this field: the Earth System Governance Project. Originating as a core project of the former International Human Dimensions Programme on Global Environmental Change, the Earth System Governance Project has matured into a global, self-sustaining research network, with annual conferences, numerous taskforces, research centers, regional research fellow meetings, three book series, an open access flagship journal, and a lively presence in social media. The article critically reviews the experiences of the Earth System Governance network and its integration and interactions with other programmes over the last decade.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.283
Teacher spread0.274 · 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.

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

Citations29
Published2019
Admission routes1
Has abstractyes

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