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Record W2626708993 · doi:10.1075/gest.16.1.04occ

Iconicity is in the eye of the beholder

2017· article· en· W2626708993 on OpenAlexaff
Corrine Occhino, Benjamin Anible, Erin Wilkinson, Jill P. Morford

Bibliographic record

VenueGesture · 2017
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIconicityLinguisticsSign languageAmerican Sign LanguageConstrual level theoryOperationalizationSign (mathematics)PsychologyPhilosophyMathematicsSocial psychology

Abstract

fetched live from OpenAlex

Abstract A renewed interest in understanding the role of iconicity in the structure and processing of signed languages is hampered by the conflation of iconicity and transparency in the definition and operationalization of iconicity as a variable. We hypothesize that iconicity is fundamentally different than transparency since it arises from individuals’ experience with the world and their language, and is subjectively mediated by the signers’ construal of form and meaning. We test this hypothesis by asking American Sign Language (ASL) signers and German Sign Language (DGS) signers to rate iconicity of ASL and DGS signs. Native signers consistently rate signs in their own language as more iconic than foreign language signs. The results demonstrate that the perception of iconicity is intimately related to language-specific experience. Discovering the full ramifications of iconicity for the structure and processing of signed languages requires operationalizing this construct in a manner that is sensitive to language experience.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.007
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
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 head0.063
GPT teacher head0.385
Teacher spread0.322 · 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 designObservational
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

Citations159
Published2017
Admission routes1
Has abstractyes

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