MétaCan
Menu
Back to cohort
Record W4398138609 · doi:10.5539/ells.v14n2p29

Repair Strategies in Chinese EFL Learners’ Story-Telling Conversation

2024· article· en· W4398138609 on OpenAlexvenueno aff
Jiangli Wei

Bibliographic record

VenueEnglish Language and Literature Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsConversationLinguisticsComputer scienceCommunicationPsychologyPhilosophy

Abstract

fetched live from OpenAlex

This study investigates the repair strategies employed by English as foreign language of Chinese learners in response to communication breakdowns, with a particular focus on their role in facilitating the progressivity of storytelling conversations. The examination delves into specific repair strategies, including self-initiated repair and repetition strategies, to shed light on their utilization and effectiveness. The findings of the investigation reveal that among the array of repair strategies utilized, English as foreign language of Chinese learners demonstrate a pronounced tendency to rely on self-initiated repairs. Notably, participants in the study primarily addressed issues related to pronouns and verb tense, with a specific emphasis on addressing tense inconsistencies, word order discrepancies, and grammatical errors. This phenomenon underscores the concerted efforts made by Chinese learners to enhance the clarity, coherence, and fluency of their communicative endeavors. Furthermore, the prevalence of self-initiated repairs exemplifies the learners’ dedication to surmounting linguistic challenges, fostering mutual understanding, and navigating the complexities inherent in interactive discourse. Such endeavors not only signify their commitment to linguistic improvement but also underscore their proactive engagement in fostering effective communication in intercultural contexts.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.850

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 communication0.0010.001
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 head0.017
GPT teacher head0.297
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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language and Literature StudiesSame topicLanguage, Discourse, Communication StrategiesFrench-language works237,207