Home Culture Attachment and Iranian Students’ Translation Ability
Bibliographic record
Abstract
English language learning is an important issue whose impact on identity change is remarkable. This study attempted to explore the relationship between Home Culture Attachment (HCA) and Iranian students’ translation ability. To this aim, 75 participants were selected and homogenized by administering Oxford Quick Placement Test. To determine the students’ HCA levels, they were administered the Home Culture Attachment Scale. Meanwhile, a literary text selected from the book “Dubliners” was used to measure their translation ability. The translations were rated by three raters based on Waddington’s Holistic Scale. Finally, Vinay and Darbelnet’s Model of Translation was used to determine the applied translation strategies. To analyze the data, Pearson correlation coefficient, multiple regression, independent-sample t-test, and one-way ANOVA were used. The findings indicated that 69.1% of the students had high HCA and 30.9% had average HCA. Also, there was a significant correlation between the students’ HCA and translation ability. Yet, HCA subscales had no correlation with the translation ability. Moreover, it was found that there was a significant correlation between the students’ HCA at the average level and their translation ability and no correlation at the high level. Finally, it was revealed that the most frequent translation strategy was modulation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".