Bibliographic record
Abstract
Colours are unique in the sense that they are used to describe humans, objects, and even the abstract, as is true vice versa. They are also interpreted in ways that allow them to convey various associations and symbolisms. This kind of interpretation extends to various religions and languages. Religious scriptures such as the Qur’an and the Bible, among others, mention colours. The Qur’an, in numerous verses, mentions various colours such as black, blue, red, and white. The Arabic language – the language of the Qur’an – has a unique way of describing colours, particularly black and white. In some verses of the Qur’an, black and white are mentioned alongside each other. In other verses, they are mentioned separately. The Arabic Islamic culture allegedly standardizes white, but is prejudiced against black. Against such a background, this paper examines the use and representation of colours in the Qur’an with a focus on black and white. This is done through a literary and hermeneutical analysis of the verses of the Qur’an that mention black and white in light of exegetical literature. It also looks at how exegetical literatures interpret these verses and how these interpretations may or may not be read in relation to race and racism.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".