From Context-Free to Context-Embedded: A Comparative Study on the Interpretation of Metaphors in Sentence-Level and Situation-Based Tests in Saudi EFL Learners
Bibliographic record
Abstract
This research delved into the understanding of metaphoric competence among Saudi EFL learners, particularly in the context of situational contexts, linguistic and conceptual similarities, and differences. With a backdrop that accentuates the significance of metaphors in language proficiency, the study addressed the lack of attention to metaphoric competence in EFL settings, especially in Saudi Arabia. The study embraced a quantitative paradigm, utilizing a cross-sectional design, and engaged a sample of 94 Saudi EFL students. Data was collected through instruments such as the Receptive Language Proficiency Test, Receptive Metaphoric Competence Test (R-MC), and Familiarity Scale (FAMscale). Key findings indicated a notable difference in students' scores between sentence-level tests (SLT) and situation-based tests (SBT) about the interpretation of metaphors. Furthermore, significant differences emerged among three linguistic and conceptual categories of metaphors for both SLT and SBT. In essence, the research underscored the paramount role of situational context and linguistic-conceptual nuances in influencing EFL learners' metaphoric interpretations. The study underscores the important differences in Saudi EFL learners' interpretations of metaphors across varied linguistic and conceptual categories. These findings advocate for targeted pedagogical approaches in EFL curricula that emphasize the integration of linguistically and conceptually congruent metaphoric expressions, paving the way for enhanced comprehension and proficiency among 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".