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Record W3011072860 · doi:10.5539/ijel.v10n3p92

Spontaneity of Speech Errors: A Diagnostic Psycholinguistic Case Study

2020· article· en· W3011072860 on OpenAlexvenueno aff
Mohammad Awad Al-Dawoody Abdulaal, Naglaa Fathy Mohammad Atia Abuslema

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersPrince Sattam bin Abdulaziz University
KeywordsPsychologyPerplexityFeelingSocial psychologyComputer science

Abstract

fetched live from OpenAlex

The aim of this study is to use speech errors as a verbal means of communication to clarify the psychological aspects of George W. Bush’s character. Bush’s character is chosen for being highly controversial, having received the lowest approval rating in 2008 and the highest approval rating after the September 11 attacks. To achieve the aim of this study, a psycholinguistic approach is adopted in addition to a speech production model set by Dell (1999), and Chang and Griffin (1999). Some of Bush’s spontaneous errors are supposed to be collected, and then categorized from a psycholinguistic perspective and finally analyzed statistically. The main results of the study can be summed in the following points. (a) The phonological and morphological errors, caused by the psychological priming, reveal how much perplexity and confusion Bush has experienced. (b) Bush’s Freudian slips—caused by psycho-physiological factors, such as fatigue, excitement, and distraction—reveal the fact that he is not an open outright president as he represses many thoughts and feelings more than he shows. (c) Bush’s syntactic errors, caused by the improper lexical insertion, reveal his poor linguistic competence; the matter that reveals low intelligence for many psychologists such as Pishghadam and Shams (2012). (d) The economical use of speech disfluencies, caused by problems in the recognition system, reveals that Bush has a tendency of rashness. That is, he may take rapid incorrect decisions that lead to catastrophes.

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)0.168
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.168
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.351
Teacher spread0.315 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

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