Delving into EFL Learners’ Cultural Conceptions Through Metaphor Analysis
Bibliographic record
Abstract
Metaphors are the windows through which it is possible to see individuals’ particular way of thinking. In this paper, a case is made through investigating L2 learners’ metaphors conceptualizing English culture. The data elicited by using the prompt “English culture is like… because…” and the dominant metaphors were identified with reference to the frequency of each metaphor. In total, 67 conceptual metaphors were placed under 14 specific categories. These 14 categories were further divided into three broad categories as: 1) culture as a positive factor; 2) culture as a negative factor; and 3) culture as an unintelligible factor. Of particular interest is the use of positive metaphors to represent culture. The findings showed that the choice of metaphors is different regarding learners’ proficiency level. With beginners, English culture is mostly realized as a negative factor. However, in intermediate and advanced levels, learners are more optimistic about English culture and the dominating ideology is a positive view. Moreover, learners’ conceptualization of second-language culture was different regarding gender. Finally, Chi-square test was computed to find out whether the differences are meaningful. The findings of this article are useful for investigating, selecting, and teaching cultural aspects in EFL classes. Hence, the results are of interest for researchers, material developers, and teachers. Key words: Culture; SLA; Metaphor; Proficiency level; Gender; EFL learners
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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 | 0.003 | 0.006 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".