Analyzing Problem-Causing Factors for Pakistani EFL Learners in Translating Present Indefinite and Past Indefinite Tenses From Urdu Into English
Bibliographic record
Abstract
Translation is considered one of the most important skills in studying and learning a language. It is termed as a craft which is thought to be enjoyed by those who aspire to grab a sound command over second language. This study has strived to analyze problem-causing factors faced by the EFL learners in translation particularly in translating present indefinite and past indefinite tenses from Urdu into English and present some suggestions to remedy the problems brought to light by the present study. The present study is quantitative in its approach. The subjects (n=200) of this study are the students of B.Sc. (4th Year) class. In order to achieve the set objectives of the study, the researcher has conducted an achievement test to achieve the set objectives of the study. The test was comprised of such ten (10) sentences of Urdu of which five were of present indefinite and the remaining five were of past indefinite tense. These sentences were taken from the paragraphs which were selected from the past papers of the university (Bahauddin Zakaria University Multan, Pakistan) examinations. This study has also aimed to present some suggestions as remedial measures to remedy these factors which cause problems for the Pakistani EFL learners in translating present indefinite and past indefinite tenses from Urdu into English. The present study recommends that two forms (singular/plural) of the first form of a verb should be given in the first column instead of infinitival meaning of this form, the exact Urdu meaning of this form of should be given and the grammar books should also give the meaning of the past form of a verb.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".