A Cognitive Semantic Account of the Preposition “‘bra” in Modern Standard Arabic with Reference to the English “Through”
Bibliographic record
Abstract
Prepositions are crucial grammatical devices for indicating location, movement, time, and other meanings. A case in point is the Arabic preposition “‘abra,” which expresses different spatial, temporal, and abstract meanings. Adopting a cognitive semantic perspective, this study investigates ‘abra, its diverse spatial meanings, and metaphorical extensions. It also uses cognitive constructs such as image schemas, prototypes, and conceptual metaphor to demonstrate how various senses of ‘abra can be described based on perceptual properties and cognitive characteristics. In addition, this study compares the meanings of ‘abra with those of its English equivalent (through). This research uses authentic examples from Modern Standard Arabic available through the Sketch Engine website and illustrations created by the researcher. The analytical description involves the basic schema, the prototypical meaning, and the metaphorical extensions. The findings reveal that ‘abra and “through” share a basic image schema and prototypical sense with slight differences resulting from varying conceptualizations. The metaphorical extensions are also similar. The distinctions between the two mostly pertain to the semantic scope in addition to the fact that ‘abra also covers the meaning of the preposition “across.” Generally, this analysis asserts that prepositional meanings are best described using a cognitive semantic framework. The results may be useful in the fields of lexicon, second learning and teaching, as well as translation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".