Determining the Intended Meaning of Words in a Religious Text: An Intertextuality-Oriented Approach
Bibliographic record
Abstract
The aim of the present study was to show how intertextuality could be a viable approach to determine the intended meaning of words in religious texts such as the Holy Quran. In order to do just this, the researcher selected two Quranic words to be the data of the study. These were al-gibaal (Arabic: ) and al-rawasi (Arabic: يساورلا ) 1 . As for the machinery, a three-level analysis was attempted. At the first level, the denotational and connotational meanings of the two lemmas (dictionary entries) as illustrated in some major Arabic dictionaries are provided. At the second, the meanings of these words were sought in the interpretations of some major Muslim expositors. Finally, some attempts were made to provide alternative explanations by bringing out the local and global intuitions that the words invoke in the Quranic text as a coherent whole. The analysis of data revealed that al-gibaal and al - rawaasi are both not part of the Earth; al-gibaal is different form al - rawaasi in that whereas al - rawaasi is the main part of a mountain digging deep in the earth, al-gibaal is the outside part; al-gibaal serve a different function as compared with that of al - rawaasi ; and finally, unlike al-rawaasi , there are three kinds of al-gibaal .
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".