Bibliographic record
Abstract
Introduction This book will be concerned with characterizing and explaining the linguistic systems that second language (L2) learners develop, considering in particular the extent to which the underlying linguistic competence of L2 speakers is constrained by the same universal principles that govern natural language in general. Following Chomsky (1959, 1965, 1975, 1980, 1981a, b, 1986b, 1999), a particular perspective on linguistic universals will be adopted and certain assumptions about the nature of linguistic competence will be taken for granted. In particular, it will be presupposed that the linguistic competence of native speakers of a language can be accounted for in terms of an abstract and unconscious linguistic system, in other words, a grammar, which underlies use of language, including comprehension and production. Native-speaker grammars are constrained by built-in universal linguistic principles, known as Universal Grammar (UG). Throughout this book, non-native grammars will be referred to as interlanguage grammars . The concept of interlanguage was proposed independently in the late 1960s and early 1970s by researchers such as Adjémian (1976), Corder (1967), Nemser (1971) and Selinker (1972). These researchers pointed out that L2 learner language is systematic and that the errors produced by learners do not consist of random mistakes but, rather, suggest rule-governed behaviour. Such observations led to the proposal that L2 learners, like native speakers, represent the language that they are acquiring by means of a complex linguistic system.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".