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Record W2476095206 · doi:10.1075/lfab.2.07blo

Symmetry and children’s poetry in sign languages

2009· book-chapter· en· W2476095206 on OpenAlexaboutno aff
Marion Blondel, Christopher Miller

Bibliographic record

VenueLanguage faculty and beyond · 2009
Typebook-chapter
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsPoetrySign (mathematics)Symmetry (geometry)LinguisticsLiteraturePhilosophyTheoretical physicsArtMathematicsPhysicsGeometryMathematical analysis

Abstract

fetched live from OpenAlex

Within the overall framework of research into properties common to poetic forms around the world, we concentrate here on the properties of symmetry and binarity. These two aspects of structure are general enough in nature to allow us to extend poetic analysis to Sign Languages (SLs), which are distinguished by their use of the visual-gestural modality. We show, via an analysis of children’s poetry in five SLs (Blondel, 2000) and a fable in Quebec Sign Language (LSQ) (Blondel, Miller & Parisot, 2006) that the structure of poetic signed discourse is based on principles of binary rhythm and spatial symmetry. Studying these structures demonstrates the utility of the syllabic-moraic model of movement in LSQ proposed in Miller (1997) and allows us to compare the relation between the poetic text and rhythm in oral nursery rhymes on one hand and, on the other, the relation between spatial and rhythmic properties in signed performances of children’s literature.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.310
Teacher spread0.293 · 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 designQualitative
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

Citations3
Published2009
Admission routes1
Has abstractyes

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