Bibliographic record
Abstract
Although is a frequently used subordinating conjunction in English. However, non-nativeness is often observed in Chinese EFL learners’ although output during pedagogical practice. This paper aims at exploring the characteristics of Chinese EFL learners’ although employment in Chinese EFL learners’ writing. The study is a corpus-based analysis launched under the analytical framework of contrastive interlanguage analysis. The interlanguage hypothesis lays the theoretical foundation of the present study. Texts from two corpora—the Chinese learner English corpus [CLEC] and the “arts and humanities” disciplinary group of the British academic written English corpus [sub-BAWEC]—are analyzed both quantitatively and qualitatively with the help of concordance software Antconc 3.2.1 and statistics program PASW Statistics 18. Based on the findings, conclusions are drawn as follows: 1) Chinese EFL learners tend to underuse although and produce mono-structural although clauses in their writing. Nevertheless, they share similar preference on deciding although placement in clauses with native English speakers; and 2) Factors such as interlingual difference between English and Mandarin Chinese, pedagogical neglect in English classrooms and different cognitive styles influence Chinese EFL learners’ although employment.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".