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Record W4206997145 · doi:10.1109/tcyb.2021.3138651

IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETY

2022· article· en· W4206997145 on OpenAlexaff
Sam Kwong, Sr Past, Eddie Past, Yo-Ping Huang Vp-Cybernetics, Enrique Herrera‐Viedma, Saeid Nahavandi, Karen Panetta Vp, Okyay Kaynak, Peng Shi, Thomas Strasser Secretary, Tom Gedeon, Владик Крейнович, Susan Kathy, Land, Kun Liu, Kathleen Kramer, Ellen Randall, Toshio Fukuda, Stephen Phillips, Educational Activities, Lawrence Hall, Maike Luiken, James Matthews, Roger Fujii, Katherine Duncan, Dalma Novak, Stephen Welby, Thomas Siegert, Donna Hourican, Jamie Moesch, Sophia Muirhead, Chris Brantley, Cherif Amirat, Karen Hawkins, Cecelia Design, Geographic Jankowski, Steven Activities

Bibliographic record

VenueIEEE Transactions on Cybernetics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsCyberneticsComputer scienceCognitive scienceEngineering ethicsEngineeringPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

man-machine systems, systems science, systems engineering, and cybernetics.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.955
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0450.020

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 head0.024
GPT teacher head0.272
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractno

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