Contributions of Grand Linguistic Theories to Second Language Acquisition Research and Pedagogy
Bibliographic record
Abstract
Research on second language acquisition (SLA) and use has always been enriched by linguistic schools and theories. The purpose of the present paper is give readers a snapshot of contributions grand linguistic theories have made to L2 acquisition research and pedagogy. The grand linguistic theories chosen for review in the present study include Structural Linguistics, Nativism, Functional Linguistics, and Cognitive Linguistics. These four linguistics theories have been, and some of them are, paid much more focus in the field of linguistics than other theories. In fact, the areas of SLA research and pedagogy have been highly influenced by these four grand linguistic theories. However, their impacts on these two areas have not been equal and, as a matter of fact, some of linguistic theories have more influenced SLA research while other theories have had implications more for SLA pedagogy. The contributions of the aforementioned grand linguistic theories to SLA research and pedagogy are discussed, along with criticisms against the contributions of each linguistic theory posed by the rival researchers.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".