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Record W1647349554 · doi:10.18806/tesl.v26i2.414

Recognizing Visual and Auditory Cues in the Detection of Foreign-Language Anxiety

2009· article· en· W1647349554 on OpenAlexvenueno aff
Tammy Gregersen

Bibliographic record

VenueTESL Canada Journal · 2009
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
FundersUniversity of Northern Iowa
KeywordsPsychologyNonverbal communicationAnxietyForeign languageAffect (linguistics)Foreign language anxietyCognitive psychologyLanguage assessmentCommunication

Abstract

fetched live from OpenAlex

.This study examines whether nonverbal visual and/or auditory channels are more effective in detecting foreign-language anxiety. Recent research suggests that language teachers are often able to successfully decode the nonverbal behaviors indicative of foreign-language anxiety; however, relatively little is known about whether visual and/or auditory channels are more effective. To this end, a group of 36 preservice English-language teachers were asked to view videotaped oral presentations of seven beginning English-language learners under three conditions: visual only, audio only, and a combination of visual and audio in order to judge their foreign-language anxiety status. The evidence gathered through this study did not conclusively determine the channel though which foreign-language anxiety could be most accurately decoded, but it did suggest indicators in the auditory and visual modes that could lead to more successful determination of behaviors indicative of negative affect.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
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)0.0020.000

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 head0.025
GPT teacher head0.362
Teacher spread0.337 · 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 designObservational
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

Citations19
Published2009
Admission routes1
Has abstractyes

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