Researching Vocabulary Development: A Conversation Analytic Approach
Bibliographic record
Abstract
This paper contributes to the much debated yet still largely unanswered question of how second language (L2) learning is anchored and configured in and through social interaction. Using a socio-interactional approach to second language (L2) learning (e.g., Hellermann, 2008; Mondada & Pekarek Doehler, 2004; Pekarek Doehler, 2010), I examine students’ search for the meaning of a lexical item and subsequent use of the same item. This study is longitudinal in design and attempts to understand how participants orient to a lexical item as an object of learning to co-construct locally enacted and progressively more complex interactional repertoires in the target language. The data consists of recorded interactions between learners of German as they work on a project outside of the classroom for several days during a two-week period. The analysis involves tracking multiple episodes where a vocabulary item is used and attended to by the group of learners. Learners engage in learning practices and create opportunities for L2 learning through interaction, employing strategies such as timely peer assistance and appropriation of new conversational meanings.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".