Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".