Saudi EFL Students’ Attitudes Toward the Target Culture and Its Relationship with Their Linguistic Backgrounds
Bibliographic record
Abstract
Culture and language have long been focal points of investigation, and both have been intensively discussed in the academic literature, but little attention has been paid to the influence of EFL students’ linguistic backgrounds on their attitudes toward the target culture, especially in the Saudi context. This mixed-method study aimed to explore the impact of learners’ linguistic backgrounds (mainly their language academic achievement levels and contexts of language learning) on their attitudes toward the target culture. The data was collected using an online questionnaire. A total of 84 students from the Faculty of Language and Translation at King Khaled University participated in this study. A Pearson correlation coefficient test and thematic analysis were used to interpret the data. The results showed a significant relationship between the participants’ linguistic backgrounds and their attitudes. The results also indicated that the participants had an overall positive attitude toward the integration of the target culture into language learning. In light of the findings, EFL students’ linguistic background should be taken into consideration before embedding the target culture into language learning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".