Pedagogical Interventions to L2 Grammar Instruction
Bibliographic record
Abstract
The role of instruction in L2 acquisition has been a key question and a theoretical issue in the field. It was directly addressed by Long (1983) in a paper in which he presented the results of several classroom-based empirical studies, all addressing the question of whether instruction can be beneficial for L2 learners. In his review, he considered eleven studies which examined whether the learners receiving instruction achieved a higher level of proficiency than those learners who did not. In these eleven studies, classroom only, naturalistic exposure only, and classroom plus naturalistic exposure were compared. Long concluded that the overall findings indicate that instruction is beneficial for adults (intermediate and advanced stages) as well as for children. It is beneficial both in acquisition-rich contexts (i.e., in which learners are exposed to the target language outside the classroom context) and acquisition-poor environments (i.e., in which learners are exposed to the target language only in a classroom context). Such benefits emerge despite the way proficiency is measured. Long concluded that a combination of instruction and naturalistic exposure to the input were optimal conditions as instruction seems to have an effect on the rate of and ultimate success in L2 acquisition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".