Bibliographic record
Abstract
This chapter explores the dynamic relationship between working memory (WM) and grammar development across adult L2 learning. For over twenty years, WM has received considerable attention in research on adult second language (L2) development. One reason for this is that L2 learning requires both processing and storage to comprehend input and to extract intake for acquisition, so differences in WM capacity may explain differences in developmental rates. Most studies on WM and morphosyntactic development in adults support the “more is better” hypothesis (Miyake & Friedman, 1998); yet others did not yield evidence in its support (e.g., Foote, 2011; Grey, Cox et al., 2015). While linguistic targets and methods may explain many discrepancies, recent research (e.g., Serafini & Sanz, 2016) may also help us understand these differences as a reflection of changes in what constitutes a cognitively demanding task (i.e., what tasks recruit WM resources) across L2 learning
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".