A Linear Regression Model-Based Approach to Assessing Translation Efficiency in Business English Translation Teaching
Bibliographic record
Abstract
As a core course of Business English majors, Business English translation plays a crucial role in the cultivation of Business English talents, and how to realize the assessment of translation efficiency in teaching has become a hot topic nowadays.This paper builds up a translation efficiency assessment index system in the teaching of business English translation around five aspects: vocabulary, syntax, context, society, and translator's factors.Random forest and Lasso regression methods were used to select 15 feature variables including sentence order and collocation between words.The multiple regression linear model was chosen to construct a model for assessing translation efficiency in business English translation teaching, and the model was estimated and tested.The least squares method was used for estimation, and all the parameters were significant (Sig<0.05)except for the variables compound sentences, sentence structure and situational intermingling.The distribution of the residuals of the model approximates to the normal distribution, which satisfies the assumption of normality and the assumption of independence, and possesses a good fit and some explanatory power.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".