An Empirical Study of Chinese EFL Learners’ Understanding and Translation of Expressions of Multiplication Entailing “Times”
Bibliographic record
Abstract
The debate on how to understand such expressions of multiplication entailing “times” as “n times more than” and “increase (by) n times” has been on and off in China since the 1980s. A review of literature seems to suggest that despite early-stage divergence in understanding, there is a general consensus among the Chinese academia at present that the English word “times” entails the base number, and therefore expressions of multiplication like “n times more than” and “increase (by) n times” are equivalent to the expression “n times as much/many as”. This paper intends to find out whether this consensus is reflected in Chinese EFL learners’ understanding of those expressions. Altogether 16 English majors from one of the key universities in the northern part of China were tested on their understanding and translation of two passages with embedded arithmetic comparisons using “n times more than” and “increase n times” respectively. It is found that a sizable proportion of them (62.5% for the former and 56.25% for the latter) gave inaccurate translation and that their rendering manifests not only their misunderstanding but also indiscretion in the translating process. Such factors as students’ indiscriminate use of information from the Internet, ambiguity and errors in popular grammar books, the presumed disjunction between EFL research and EFL teaching, and the untimely updating of English competence on the part of Chinese EFL teachers in China are proposed as possible reasons.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".