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Record W7163166287 · doi:10.6082/aqdfq-y6458

Mediterranean Travel, Interconfessional Rivalry, and the Written Languages of Late Mamluk Mount Lebanon

2025· article· en· W7163166287 on OpenAlexaff
Ghassan Osmat

Bibliographic record

VenueUniversity of Chicago · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMountMamlukArabicFifteenthPortraitCorsicanArabic languagesIslam

Abstract

fetched live from OpenAlex

In the twentieth century, the languages of medieval Mount Lebanon became a subject of contention between Arab and Lebanese nationalists. A largely unstudied manuscript corpus from the fifteenth century illuminates this matter. In this corpus, bilingualism prevails, with Middle Arabic serving as the primary written language and Syriac serving as the language of worship and prestige. This article draws on colophons, chronicles, and the fifteenth-century literary texts to examine the norms governing language in late Mamluk Mount Lebanon. It is centered on the lives and works of Mūsá Ibn ʿAṭshah, Yūḥannā ibn Ḥasan, Nūḥ al-Baqūfānī, and Jibrāyil Ibn al-Qilāʿī. Through these figures, it considers authorial intention, educational background, audience, and dynamics of power as determinants of language choice. From this social history of language, a portrait of Mount Lebanon emerges as the site of Syriac revival and a new Christian Arabic literature at the confluence of Mamluk culture, Eastern Christianity, Franciscan vocation, and the Mediterranean.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.242
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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