How Colours are Semantically Construed in the Arabic and English Culture: A Comparative study
Bibliographic record
Abstract
Most works in cognitive semantics have been focusing on the manner, in which an individual behaves - be it the mind, brain, or even computers, which process various kinds of information. Among humans, in particular, social life is richly cultured. Sociality and culture are made possible by cognitive studies; they provide specific inputs to cognitive processes (Wilson & Keil, 1999). The current work focussed on the use of colours as a term throughout the Arabic and English culture. In fact, one colour may imply different meanings at the same place, and this makes us ponder on how colours are construed in cross cultural diversity? In this vein, the current work referred to the etymological meaning of the colour terms, and provided six basic Arabic colour terms and cross to six English colour terms. Using the cognitive cultural categorization for each colour term, three different meanings were identified - basic meaning, extended meaning and additional meaning. ‘Basic meaning’ refers to the original meaning of the colour term, whereas ‘extended meaning’ refers to the meaning extended from the original meaning throughout human experience and ‘additional meaning’ refers to the meaning which has been further abstracted from the extended meaning. Thus, the aim of this work was to show how meanings of colours are identified in the different cultures of Arabic and English, and in the way whereby both languages are relevant and different for each colour term.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".