BRIDGING THE GAP
Bibliographic record
Abstract
For some, research in learning and teaching of a second language (L2) runs the risk of disintegrating into irreconcilable approaches to L2 learning and use. On the one side, we find researchers investigating linguistic-cognitive issues, often using quantitative research methods including inferential statistics; on the other side, we find researchers working on the basis of sociocultural or sociocognitive views, often using qualitative research methods including case studies and ethnography. Is there a gap in research in L2 learning and teaching? The present article developed from an invited colloquium at the 2013 meeting of the American Association for Applied Linguistics in Dallas, Texas. It comprises nine single-authored pieces, with an introduction and a conclusion by the coeditors. Our overarching goals are (a) to raise awareness of the limitations of addressing only the cognitive or only the social in research on L2 learning and teaching and (b) to explore ways of bridging and/or productively appreciating the cognitive-social gap in research. Collectively, the nine contributions advance the possibility that the approaches are not irreconcilable and that, in fact, cognitive researchers and social researchers will benefit by acknowledging insights and methods from one another.
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.
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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.060 | 0.013 |
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".