Reciprocal Effect Between Fossilization of the Lexi Cogrammatical Error and Linguistic Focus Within Professional EFL Learners
Bibliographic record
Abstract
Fossilization has become the focus of many L2 studies since its introduction in 1972 as many learners fail to achieve native-speaker competence. Researchers have tried to unravel the causes of fossilization, among which focusing has been claimed to be of great importance. This study aimed to explore the effect of focusing on fossilization. To achieve this aim, a mixed-methods approach was utilized. Sixty advanced L1 Persian learners of English studying in Iran were chosen to perform two written and three spoken tasks twice. Qualitative data included the content analysis of the participants’ performance on the written and spoken tasks while the quantitative data included percentages of focused errors and recurrent erroneous forms. The errors observed in both performances were counted and classified. Three main categories named Grammatical Errors, Lexical Errors, and Cohesive Errors were identified. The observed errors were further classified into 36 subcategories. When learners’ ability in focusing their errors was investigated, it was found that they could focus 37.4% of the 3,796 fossilized forms they had produced. Most of the errors observed were categorized in the category of grammatical errors. Focusing affected the number of errors produced. It can be concluded that becoming aware of ones fossilized forms, one will produce fewer fossilized forms. The results of the current study have implications for English language teachers and learners. By being informed of the errors learners make while learning a language and how their focusing affects fossilization, teachers can improve their teaching practice which in turn enhances learning.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".