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Record W4410766994 · doi:10.5430/wjel.v15n7p203

Contextual Significations of al-birr and al-qisṭ in the Qur'an: A Semiotic Approach

2025· article· en· W4410766994 on OpenAlexvenueno aff
Hissah Mohammed Alruwaili, Mohamed Elarabawy Hashem, Kusmana Bin Oking Azhuri, Ayman Khafaga

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldPsychology
TopicFamilies in Therapy and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSemioticsComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Drawing on Saussure's (1916/1959) semiotic approach, this paper explores the various meanings pertaining to the concepts of al-birr (kindness) and al-qisṭ (justice) in the Qur'an. More specifically, this paper attempts to probe the extent to which Saussure's langue/parole binary can enhance our understanding of the Qur'anic language. The paper seeks to fulfil three main objectives: (i) identifying the various significations communicated by al-birr and al-qisṭ in the Qur'an; (ii) showing the extent to which the historical and theological context helps to mark the various significations associated with each term; and (iii) highlighting the practical meanings the two terms convey within their different contexts in the Qur'an. This paper has two main findings: first, the two words of al-birr and al-qisṭ communicate various meanings that are contextually shaped; and second, both al-birr and al-qisṭ portray Islam as a religion of peace and tolerance and being committed to fostering societal harmony through the principles of kindness and justice. Further, the findings of this study are anticipated to contribute to cross-cultural communication by fostering intercultural dialogue amongst people from diverse cultural backgrounds and, therefore, challenge the narratives in some media that represent Islam as intolerant or harsh towards non-Muslims and narrow a cultural gap that has always been nurtured and sustained by a We-Them discourse.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.310
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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