Sorry Used by L2 Adult Learner: Managing Learning Opportunity and Interpersonal Relationship in Classroom Interaction
Bibliographic record
Abstract
This study investigates functions of sorry in L2 Chinese classroom interactions through the conversation analysis approach with an aim to investigate the relationship between sorry and L2 learning and possible functions of sorry in managing interpersonal relationships in classroom interactions. Through analysis of 36 hours’ video-recorded classroom interaction, this research shows that the non-apologetic sorry could be employed by adult learners to obtain various learning opportunities, such as active participation, production of appropriate responses, active use of target language, and attempts to solve problems that are not designed in the teaching agenda. Moreover, sorry could be used as a strategy for constructing polite co-operation and to mitigate possible offenses against tutors during classroom interactions, as well as to manage interpersonal relationships based upon the theoretical framework of politeness. Findings from this study can also help us understand how sorry serves pragmatic purposes for L2 classroom interaction and provide us with pedagogical implications for L2 learning and teaching. Future studies need to examine sorry as used by L2 learners in conversational turns other than the same turn, as well as at different positions of a turn, to provide evidence for its functions in classroom interaction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".