MétaCan
Menu
Back to cohort
Record W4395080784 · doi:10.5539/elt.v17n5p51

Iconicity in Chinese Sign Language and Filipino Sign Language

2024· article· en· W4395080784 on OpenAlexvenueno aff
Jianwei Wang

Bibliographic record

VenueEnglish Language Teaching · 2024
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsIconicitySign languageLinguisticsSign (mathematics)PsychologySociolinguistics of sign languagesAmerican Sign LanguageLanguage interpretationPhilosophyMathematics

Abstract

fetched live from OpenAlex

Sign language is primarily used as a means of communication by the deaf and hard of hearing. Iconicity is considered as its typical feature. This paper makes a preliminary comparison on lexical items between Chinese Sign Language (CSL) and Filipino Sign Language (FSL) through examining the iconic devices used by the CSL and FSL signs. The study provides some valuable evidence that the iconicity is prevalent in CSL and FSL which always use similar iconic device for the same concept due to shared embodied experience though different iconic devices are occasionally used. These iconic devices include direct (1) presentation; (2) number representation; (3) shape representation; (4) movement representation (5) size representation; (6) part-for-whole representation; (7) metonymic/metaphorical representation. The findings of the research could help to reveal the relationship between language and cognition and make some contributions to the communications among the deaf and hard of hearing in both Chian and the Philippines.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.324
Teacher spread0.313 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicHearing Impairment and CommunicationFrench-language works237,207