“Instructing” the Cruxes of Language Errors: Diagnosing the EFL Students’ Significant Translation Errors
Bibliographic record
Abstract
The study follows growing the author’s concern over the EFL students’ significant translation errors although a number of researches in the field of Error Analysis showed the equivalent/unchanged results, namely, MT and TL interferences were the major causes of the EFL students’ Writing and Translation errors. EFL students keep making errors. On the basis of the facts, the study aims at specifically instructing the cruxes of the errors, diagnosing the students’ errors and observing whether or not significant improvements were found after instructing the errors. This study entailed the use of a qualitative method design. The purposive sampling and typical sample technique were ways of selecting the population and sample. Observation and unstructured interviews were techniques of collecting the data while the 1973 Corder’s clinical elicitation; and Miles and Huberman’s flow model were techniques of analysing the data. The results of the study showed that the significant translation errors made by the 2nd-year ED class II-A students before instructing the cruxes of errors were heavily centred on MT causes, TL interferences, and Communication Strategy of Holistic strategy of Approximation and Analytical strategy of Circumlocution-based errors. The total number of these errors was 1,948. In contrast, after instructing them, it significantly decreased to 636. The decrease in the number of errors in the students’ translation positively signified that the instruction of the cruxes of the errors could deduct students’ critical English language issues from making errors. The instruction in the cruxes of the errors effectively mitigates the significant effects of the MT and TL interferences, and Communication strategy-based errors; significantly improves the students’ knowledge of the cruxes of the LLU errors in Translation, as well as qualifies the outputs of their Indonesian-English translation.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".