Bibliographic record
Abstract
This study aimed at presenting a comparative analysis of some metaphorical expressions used for conceptualizing women in English and Arabic. It adopted a qualitative research model. A set of English and Arabic expressions conventionally used when describing women were collected and grouped according to the Conceptual Metaphor Theory by Lakoff and Johnson (1980) into three general source domains: ANIMALS, PLANTS, and OBJECTS. Then a cross-cultural comparison was made by adopting Barcelona's (2001) framework. Data analysis revealed that the two languages share several basic (universal) metaphors in conceptualizing women. However, some differences between Arabic and English have been detected when conceptualizing women metaphorically. The differences might be due to the different sociocultural interpretations of the source and target domains in each language. In sum, this area of study must be a subject of further research by Arab scholars since Arabic involves a great number of metaphorical exploitations, which needs to be investigated from different perspectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".