Turning Miscommunication Events into Opportunities for Developing Interactional Competence
Bibliographic record
Abstract
Many studies have explored the difficulties faced by foreign language learners when they begin the learning journey from survival to advanced level. Most of these investigations, however, tend to focus on what makes the road to fluency strewn with obstacles and challenges; no significant attention has been paid to what makes the journey successful. This paper analyzes the discursive strategies that advanced learners of English use to turn miscommunication events into opportunities to further develop their ability to negotiate meaning and manage interactions. It explores some of the strategies and resources that the research participants in the study use to signal, prevent and repair misunderstanding. In other words, this paper pays attention to communication successes rather than failures. The methodology employed here is ethnographic, and the theoretical framework derives from interactional sociolinguistics which takes a socially- and contextually-oriented approach to the study of language. The principal method used to collect data was participant observation with audio recording, combined with serendipitous interviews and focus group discussions. The research participants were teachers and students of an employment preparation program for immigrants to Canada. The study took place over 12 weeks from September to November 2009 at a community college in a western Canadian province. The central argument this paper advances is that language learning at the advanced level is developed through the active practice of negotiating meaning, repairing misunderstanding, and collaboration. Pedagogically, language teachers could benefit from being familiar with the basic tools of discourse analysis to gain insights into the management of talk-in-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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".