Exploring the Structural Mappings of Eating Metaphors in Darija
Bibliographic record
Abstract
This paper explores the pervasive presence of food metaphors in Darija (Moroccan Arabic) and examines their role as a fundamental mechanism of human thought rather than a mere stylistic embellishment. Food, as an essential aspect of human experience, carries both positive connotations—such as intellect, virtue, and happiness—and negative associations, including suffering, mortality, and conflict. Through an analysis of Darija data, this study investigates how food metaphors are mapped onto abstract domains such as ideas and temperament, a phenomenon observed across unrelated cultures due to the universal experiential significance of food. The findings support Lakoff’s (1993) assertion that even the most poetic or creative metaphors arise from well-established conceptual structures embedded in cultural cognition. Additionally, this paper identifies variations in the TEMPERAMENT IS FOOD metaphor, demonstrating that while this conceptual mapping exists across cultures, its specific manifestations are shaped by differences in culinary traditions, sensory perceptions, and cultural attitudes toward taste. A comparative analysis of EATING metaphors in Darija and Chinese reveals both similarities and divergences, which this study attributes to cognitive commonalities and cultural influences. While Moroccan linguistic traditions reflect the life and teachings of the Prophet Muhammad (peace be upon him), resulting in a metaphorically rich language, Chinese metaphorical expressions are shaped by the philosophical traditions of Buddhism, Taoism, and Confucianism. This exploration highlights the intricate interplay between language, cognition, and culture, offering insights into how food metaphors structure human understanding across diverse societies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".