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Record W2281429716 · doi:10.1145/2839462.2856339

POEME

2016· article· en· W2281429716 on OpenAlexafffund
Shannon Cuykendall, Ethan Soutar-Rau, Thecla Schiphorst

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMovement (music)Kinesthetic learningPoetrySpace (punctuation)AestheticsArtVisual artsComputer sciencePsychologyLiterature

Abstract

fetched live from OpenAlex

The interactive movement installation, POEME: A Poetry Engine explores the relationship between bodily, mechanical and digital interpretations of movement. The installation grew out of our design of POEME, a mobile website that responds to movement with poetic verse. While the POEME website can be used virtually anywhere, the installation anchors the interaction to a tangible space. We reference the choreographed routines of mass transit by giving participants a virtual ticket which grants them entrance to a private performance space. The participant's movement is conveyed outside of the space in the form of measurements and poetic verse created from words that relate to mechanical theories of movement. In order to understand the relationship between these interpretations and the bodily movement that powers POEME, the audience must experience the interaction for themselves. POEME builds off prior work in body-centric, experiential design. In contrast to systems that seek to identify individual features of movement, we instead attempt to characterize and respond to whole kinesthetic experiences through poetic verse.

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)0.003
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.787
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2130.061

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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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

Citations25
Published2016
Admission routes2
Has abstractyes

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