Describing and assessing interactional competence in a second language
Bibliographic record
Abstract
Abstract The contributions to this Special Issue employ conversation analysis to illustrate how detailed analysis of language use can lead to the identification of assessable features of second/foreign language Interactional Competence (L2 IC) and the development of institutional testing instruments and practices. L2 IC has been the focus of much research at the intersection of social interaction and second language acquisition. It has also been treated as a construct in the field of language assessment. However, scholars in each research branch have just begun to collaborate systematically. This Special Issue furthers this collaboration, connecting research on L2 IC in diverse learning contexts with practical questions regarding the assessment of individual learners. It adopts a dialogic ‘full paper–commenting paper’ structure: Four empirical papers are each paired with invited commentaries that provide critical discussion and a complementary view of the topics the full papers address. The final discussion papers take a broader perspective on the complex nature of L2 IC and assessment and propose ways to productively move forward. Besides introducing the notion of L2 IC and each individual contribution, this introductory article explains the rationale behind the Special Issue in relation to current research.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".