Working on grammar at school in L1 education: empirical research across linguistic regions. Introduction to the special issue
Bibliographic record
Abstract
Empirical studies pertaining to working on grammar at school and its effects have not been the focus of L1 research in recent years. For instance, none of the current international large-scale studies investigates grammar learning. This might be the result of widespread doubts about the benefits of grammar learning for students, but—however justified one may consider such doubts to be—they should not lead researchers to neglect this topic. To be fair, research in the field has probably been hindered by the fact that there is virtually no exchange about findings across di-verse linguistic regions, so that empirical results which emerged in one country have seldom been recognized in other countries. Variation across linguistic regions can be found not only in research results but also in research questions—a situation which one may consider tolerable in itself but which constitutes a problem when lack of communication between researchers on grammar learning and grammar instruction leaves a real gap, as is true in this case. This special issue aims to offer an international overview of empirical research on grammatical learning at school within the context of L1 education (including learning about L1 grammar outside of L1 lessons, but excluding foreign language learning), and to deepen it by discussing recent approaches. Thus, the special issue is intended to provide a stimulus for further research on the subject and a starting point for the dissemination of international research into local research communities.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".