Bibliographic record
Abstract
SLA researchers have long been baffled why native-like attainment is rare among adult L2 learners. They have been contributing to approaching the conundrum through the lens of fossilization (Selinker, 1972). Fossilization has aroused more and more attentions at home and abroad since proposed. This thesis consists of major five chapters. The Introduction section mainly introduces the origin and significance of study and outlines the fossilization research achievements at home and abroad from different facets, in which many foreign researchers provide their theoretical basis for the language fossilization, especially for the double effect on foreign language writing, whereas the domestic researchers make a lot of empirical studies that explore the correlation between language fossilization and instruction at different levels. Based on the foregoing analysis, the last chapter puts forward two objective proposals about teaching strategies and classroom activities for high school teachers. On one hand, the English writing teaching approach should be distinguished from the colloquial speech teaching approach because L2 writing pertains to a more formal language output, and the correct output of language forms should not be overlooked when concerning that of language meaning. Only on the basis of the correct language forms, language meaning will be naturally closer to the target language.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".