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Record W3184343859 · doi:10.5539/ijel.v11n5p14

Onomatopoeia and Cat Vocalisations

2021· article· en· W3184343859 on OpenAlexvenueno aff
Aliaa Aloufi

Bibliographic record

VenueInternational Journal of English Linguistics · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersTaibah University
KeywordsOnomatopoeiaIconicityArbitrarinessLinguisticsPsychologyImitationNatural (archaeology)PhenomenonSocial psychologyHistoryPhilosophy

Abstract

fetched live from OpenAlex

Onomatopoeia—the imitation of natural sounds—is a common phenomenon in human language, though imitations of the same sounds might appear different cross-linguistically. It is true that onomatopoeia is not like ordinary language, but how does it differ from natural vocalisation? While the distinction between onomatopoeia and ordinary language has received ample treatment, its difference from natural sounds have so far received less attention from linguistics. This study aims to investigate the phonetic differences between onomatopoeic cat sounds in ten languages and natural cat vocalisations. The findings show some segmental and phonotactical distinctions due to the direct representation of these words regarding their meanings, which clearly indicates that this phenomenon in world languages is not arbitrary and offers strong evidence of iconicity. While arbitrariness is the norm in human language and has an essential impact on language development, there are clearly some nonarbitrary aspects of human language, and onomatopoeia is notable among them.

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)0.001
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicLanguage, Metaphor, and CognitionFrench-language works237,207