A Cognitive Metaphorical Analysis of Selected Verses in the Holy Quran
Bibliographic record
Abstract
Metaphor has been generally contemplated and analyzed inside the schema of verbal discourse, scholarly works and humanistic studies. It has been identified with metaphorical language and has been viewed as quite recently a sort of aesthetic work, or something that is separated and disengaged from common language (Murray & Moon, 2006). In addition, conventional learning of metaphors presents language as an abnormal or different method for using language (Goatly, 1997). Likewise, as Goatly has put it, scholars need metaphor strictly limited to writing and talk. Moreover, metaphor is viewed as something that fits in with abstract structures which is more concerned with novel or intriguing usages of words. Lakoff & Johnson (1980) presented an alternate idea and perspective of metaphor which is known as reasonable metaphorical classification. The theory underlying this new approach is that the reasonable metaphors enter our understanding of our general surroundings and they shape our demeanor of it. Appropriately, as pointed out by Lakoff & Johnson (1980), metaphor, which is available in ordinary discourse, in every language, and is to a certain degree, has gotten to be culturally specific. Besides they contend that metaphors affect our method for considering the world and are discovered widely in a significant number of our languages, contemplations and activities.
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 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 | 0.001 | 0.062 |
| 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 | 0.000 |
| 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".