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Record W2894904398 · doi:10.7202/1051193ar

Phonemes as Modeling Devices

2018· article· en· W2894904398 on OpenAlexaffvenue
Marcel Danesi

Bibliographic record

VenueRecherches sémiotiques · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLinguisticsSemiosisMeaning (existential)Extension (predicate logic)Computer scienceSemioticsRubricSound (geography)Word (group theory)Theoretical linguisticsSound symbolismProperty (philosophy)Element (criminal law)EpistemologyPsychologyPhilosophyAcoustics

Abstract

fetched live from OpenAlex

The study of how sounds in language are used to create words and other linguistic structures that are imitative of some sound property of their referents comes generally under the rubric of sound symbolism theory. While it has been dismissed by various approaches to language, the empirical and anecdotal support for sound symbolism is now too massive to ignore. From the database of evidence it now makes available, various hypotheses can be formulated about originating elements in word formation and, by extension, in seemingly diverse linguistic systems. One of these is the phoneme which bears suggestive meaning in itself as a primary originating element. It is thus a “modeling device” that leads, by extension, to the derivation of larger structures of meaning. Using an adapted version of modeling systems theory as developed by the Tartu School of semiotics, this paper argues that the phoneme is in fact more than a cue for distinguishing words – rather it is an elemental modeling device. This view potentially has some basic implications not only for sound symbolism theory, but also for the study of semiosis itself.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.151
GPT teacher head0.411
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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
Published2018
Admission routes2
Has abstractyes

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