Bibliographic record
Abstract
The main purpose of this study is to investigate the cognitive-semantic content of xordan in Persian, and whether it demarcates similar conceptual domain as the English verb ‘to eat’. The verbs related to the bodily experience of eating or consuming food is the source of metaphorical conceptualizations and mappings in various semantic domains rooted in universal experiential realities. Cross linguistic, cross cultural studies have reported both commonalities and variations in the conceptualization of the act of eating. Adopting the basic tenets of the Conceptual Metaphor Theory, proposed by Lakoff and Johnson (1980, 1999), this study is an attempt to delve into the conceptual system of Persian in order to explore its specific cultural embodiment, and socio-cultural influences in the use of metaphorical concepts of xordan. With a focus on the basic syntax and semantic properties of xordan, this study employs a lexical structure, i.e. the radial category in a chaining model to illustrate the complexities of metaphorical extensions of eating in Persian. Our observations reveal that the metaphorical expressions of the verb of xordan ‘to eat’ occur extensively in Persian, manifesting the Persians’ unique way of thinking and mind. These particular em-minded cultural models have widely left their traces in the Persians’ belief systems, the effects therefore, have been extended into Persian metaphoric language and cognitive conceptualizations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".