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Record W4415714656 · doi:10.1515/jhsl-2024-0040

Sanskrit, Classical Arabic, Latin and now English – a case of a special kind of lingua franca use and status

2025· article· en· W4415714656 on OpenAlexaff
Nikola Dobrić, Martin Korenjak, Stephan Procházka, A. M. Ruppel, Kristin Reinke, Rosemary Wildsmith-Cromarty, Enrique Pato

Bibliographic record

VenueJournal of Historical Sociolinguistics · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsLingua francaPhenomenonNorm (philosophy)ArabicClassical ArabicTheoretical linguistics

Abstract

fetched live from OpenAlex

Abstract This article puts forward the hypothesis that a small number of lingua francas (LFs) can be historically observed as having reached a special status and manner of use. Termed here lingua cosmopolitanas (LCs), the proposed distinctive group is exemplified by languages such as Sanskrit, Classical Arabic and Latin, representing a distinctive quasi-universal communicative phenomenon not adequately observed in research. To test this hypothesis, our study compares six LFs in terms of broad sociolinguistic characteristics. These include, on the one hand, post-4th-c. Sanskrit, post-8th-c. Classical Arabic and post-9th-c. Latin, conjectured as potentially LCs, and, on the other hand, post-18th-c. French, post-16th-c. Spanish and post-18th-c. KiSwahili in international use, as likely not of such additional status. The results of the comparison show the former as distinctive, with five characteristics separating them from the three latter LFs. They are found to include (a) loss of native speaker reference; (b) becoming the implicit conduit of the times; (c) fixedness of norm in writing; (d) extreme longevity in stable form; and (e) emergence of and co-existence with ‘daughter’ languages. The findings are then discussed in terms of paradigms describing natural language as a phenomenon in general and LF as a concept in particular. In addition, English is also discussed in the same terms, as potentially an LC in statu nascendi, and tested against the deduced signifiers.

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: Other · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.139

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 communication0.0010.001
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 head0.025
GPT teacher head0.251
Teacher spread0.227 · 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
GenreOther

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

Citations4
Published2025
Admission routes1
Has abstractyes

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