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Record W1867779174 · doi:10.5539/ies.v8n10p114

A Model of Situational Willingness to Communicate (WTC) in the Study Abroad Context

2015· article· en· W1867779174 on OpenAlexvenueno aff
Graham Robson

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsWillingness to communicatePsychologySituational ethicsContext (archaeology)Structural equation modelingTraitSocial psychologyPersonalityConstruct (python library)Context effectBig Five personality traitsRealmMathematics educationComputer scienceLinguistics

Abstract

fetched live from OpenAlex

The use of structural modeling has helped to explain constructs leading to Willingness to Communicate (WTC) in L1 and L2 contexts. When WTC was conceptualized as a trait in the L1, more personality variables were used in models. When WTC moved into the realm of second language, researchers still used trait measurements to explain the construct, along with motivation and other communication-related variables. More recently, researchers recognize that WTC is also a situational variable and some researchers have created measurement tools accordingly. This study focuses on 67 students studying on a pre-university academic course in English and tests a structural model using classroom constructs as they are deemed the most important for communication in the classroom to predict WTC. Also, the model uses a teacher score to measure the relationship between self-report WTC and actual classroom communication. The model was found to have reasonable levels of fit, showing the importance of classroom variables in situational WTC in the second language context.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.394
GPT teacher head0.456
Teacher spread0.062 · 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 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

Citations14
Published2015
Admission routes1
Has abstractyes

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