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Record W4247556152 · doi:10.1145/3279810

Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data

2018· paratext· en· W4247556152 on OpenAlexaff
Felix Putze, Jutta Hild, Catherine Lai, Gabriel Murray, Dmitrii Fedotov, Olga Perepelkina, Eva Kazimirova, Maria Konstantinova, Wolfgang Minker

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsComputer scienceTask (project management)Focus (optics)CognitionTask analysisNatural language processingData modelingArtificial intelligenceData scienceHuman–computer interactionPsychologySoftware engineeringEngineering

Abstract

fetched live from OpenAlex

We address the task of automatically predicting group satisfaction in meetings using acoustic, lexical, and turn-taking features. Participant satisfaction is measured using post-meeting ratings from the AMI corpus. We focus on predicting three aspects ...

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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0060.007
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0210.006

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 head0.082
GPT teacher head0.363
Teacher spread0.281 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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