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Record W4388777886 · doi:10.1515/mill-2023-0003

Pilgrimage in Pre-Islamic Arabia: Continuity and Rupture from Epigraphic Texts to the Qur’an

2023· article· en· W4388777886 on OpenAlexaff
Süleyman Dost

Bibliographic record

VenueMillennium · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPilgrimageVenerationIslamContext (archaeology)ProcessionAncient historyHistorySacrificeParallelsTurkishArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract References to the pilgrimage in the Qur’an, called ḥajj and ʿumra , are often very brief, but recent studies have shown that most of what is gleaned from the Qur’an about the practice can find parallels in pilgrimages to other sites in Arabia. In this article, I read the Qur’anic data on ḥajj and ʿumra in the light of Arabian inscriptions that mention pilgrimage rituals. In particular, the annual pilgrimage to the Awām Temple in Ma’rib in South Arabia, about which we know a great deal, can shed light on the larger context of the ritual in pre-Islamic Arabia. I argue based on a discussion of Qur’anic and epigraphic materials that the ḥajj and ʿumra of the Qur’an share many elements with other Arabian pilgrimages, but the Qur’an clearly expresses discontent with certain practices of pre-Islamic pilgrimage such as ritual hunt while endorsing or approving others such as the procession between the hills of al-Ṣafā and al-Marwa. Most importantly, I contend that the Qur’an attempts to decouple pilgrimage and animal sacrifice especially due to the latter’s strong association with physical objects of veneration called awthān and nuṣub in the Qur’an.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.232
Teacher spread0.212 · 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 designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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