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Record W3151027712 · doi:10.1017/9781316882764

The Study of Speech Processes

2021· article· en· W3151027712 on OpenAlexaff
Victor J. Boucher

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typearticle
Languageen
FieldComputer Science
TopicLinguistic Studies and Language Acquisition
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLinguisticsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

There has been a longstanding bias in the study of spoken language towards using writing to analyse speech. This approach is problematic in that it assumes language to be derived from an autonomous mental capacity to assemble words into sentences, while failing to acknowledge culture-specific ideas linked to writing. Words and sentences are writing constructs that hardly capture the sound-making actions involved in spoken language. This book brings to light research that has long revealed structures present in all languages but which do not match the writing-induced concepts of traditional linguistic analysis. It demonstrates that language processes are not physiologically autonomous, and that speech structures are structures of spoken language. It then illustrates how speech acts can be studied using instrumental records, and how multisensory experiences in semantic memory couple to these acts, offering a biologically-grounded understanding of how spoken language conveys meaning and why it develops only in humans.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes1
Has abstractyes

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