Approaching the Language of the Second Language Learner: Interlanguage and the Models Before
Bibliographic record
Abstract
The present paper attempts to provide a critical evaluation of the most prominent pedagogical models that have dealt with the language of the second language (L2) learner starting from the second half of the 20th century. The three most influential approaches in the domain are investigated in this study: contrastive analysis (CA), error analysis (EA), and interlanguage (IL). Each of these models is tackled in terms of its beginning, psychological background, essential tenets, mechanism, and its pedagogical value. Prominently, this work is aimed at teasing apart the confusion that surrounds the fields of acquiring second/foreign language. It also endeavors to clear out the overlapping of both terminology and concept that cloud these areas. Focus is placed on IL owing to the dominant share of attention it has received from researchers and applied linguists who have found many of their questions answered and many information-gaps filled in with this theory. This review paper is an extract of an in-progress PhD dissertation on interlanguage pragmatics of Kurdish university EFL learners, which is an applied study addressing both the pragmalinguistic and sociopragmatic knowledge of the students.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".