The grammar dimension in instructed second language learning
Bibliographic record
Abstract
List of contributors Foreword Roger Hawkins Acknowledgements Introduction: Grammar Dimension in Instructed Second Language Learning Alessandro Benati, Cecile Laval, and Maria Arche Part One: Theoretical and Pedagogical Developments Chapter 1. Against Rules Bill VanPatten and Jason Rothman Chapter 2. Possibilities and Limitations of Enhancing Language Input: a MOGUL perspective Mike Sharwood-Smith Chapter 3. Processing Instruction: Where research meets practice James Lee Chapter 4. Collaborative Tasks and Their Potential for Grammar Instruction in Second/Foreign Language Context Maria del Pilar Garcia Mayo Chapter 5. Interactional Feedback: Insights from theory and research Hossein Nassaji Part Two: Empirical Research Chapter 6. Instructed SLA as parameter setting: Evidence from earliest-stage learners of Japanese as L2 Megan Smith and Bill VanPatten Chapter 7. The Relationship between Learning Rate and Learning Outcome for Processing Instruction on the Spanish Passive Voice James Lee Chapter 8. Coproduction of Language Forms and Its effects on L2 Learning Hossein Nassaji and Jun Tian Chapter 9. Raising Language Awareness for Learning and Teaching L3 Grammar Tanja Angelovska and Angela Hahn Index
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".