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Record W3120516826 · doi:10.1017/9781316882764

The Study of Speech Processes: Addressing the Writing Bias in Language Science

2021· book· en· W3120516826 on OpenAlexaff
Victor J. Boucher

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLinguisticsSpoken languageMeaning (existential)Computer sciencePsychologyNatural language processing

Abstract

fetched live from OpenAlex

"For many readers of the present work, the notion that language is separate from speech (or that language competence is separate from performance) will likely have been acquired in an introductory course to language study. This notion can have a major influence on how language is conceptualized, and presents a central tenet in the field. In examining introductory texts used by generations of students (e.g. the multiple editions of Akmajian, Demers, Farmer et al., 2010; Fromkin, Rodman, & Hyams, 2013, and others), one finds typical arguments for distinguishing speech from language. These arguments serve to specify not only the object of study, but also how to study it. However, all these arguments, it should be pointed out once more, involve written material"--

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.013
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.379
Teacher spread0.279 · 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 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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