Describing and assessing interactional competence in a second language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it