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Record W2961224929 · doi:10.1145/3371906

Toward model-driven sustainability evaluation

2020· article· en· W2961224929 on OpenAlexaff
Jörg Kienzle, Gunter Mussbacher, Benoît Combemale, Lucy Bastin, Nelly Bencomo, Jean‐Michel Bruel, Christoph Becker, Stefanie Betz, Ruzanna Chitchyan, Betty H. C. Cheng, Sonja Klingert, Richard F. Paige, Birgit Penzenstadler, Norbert Seyff, Eugene Syriani, Colin C. Venters

Bibliographic record

VenueCommunications of the ACM · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversité de MontréalMcMaster UniversityUniversity of TorontoCanada Research ChairsMcGill University
FundersEngineering and Physical Sciences Research Council
KeywordsSustainabilitySustainability scienceSustainability organizationsSocial sustainabilityEnvironmental economicsProcess managementManagement scienceEnvironmental resource managementComputer scienceBusinessEconomics

Abstract

fetched live from OpenAlex

Exploring the vision of a model-based framework that may enable broader engagement with and informed decision making about sustainability issues.

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)0.038
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.004
Scholarly communication0.0110.010
Open science0.0030.008
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.592
GPT teacher head0.500
Teacher spread0.092 · 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 designTheoretical or conceptual
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

Citations24
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCommunications of the ACMSame topicComplex Systems and Decision MakingFrench-language works237,207