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Record W4408986099 · doi:10.31763/15

Religion as a context for language contact

2025· article· en· W4408986099 on OpenAlexaff
Elinor Nesher

Bibliographic record

VenueJournal of Religion and Linguistics · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Linguistics, Cultural Analysis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLanguage contactContext (archaeology)LinguisticsSociologyPsychologyHistoryPhilosophyArchaeology

Abstract

fetched live from OpenAlex

Even though there is a lot of research on religious language, not much research has been done on how language and religion interact when someone is bilingual or multilingual until recently. This chapter initially presents a summary of prior research, predominantly focused on the translation of sacred writings into diverse languages. Some research has already looked into how the spread of religion has affected language, especially when it comes to choosing ceremonial language and writing systems. We examine the linguistic patterns and practices traditionally associated with several religious traditions, including Buddhism, Christianity, Hinduism, Islam, Judaism, and Quakerism. After that, the chapter talks about the language effects of missionary work in different postcolonial settings, mainly those that have to do with Christian groups. Recent studies have investigated the linguistic effects of connections between regionally dominant languages and dialects and religious practices in various global contexts. The researcher also examines the interplay between immigration and the preservation or alteration of language within religious contexts. The chapter concludes by observing that recent political developments, interest in conversion initiatives for non-Christian religions, and an increasing acknowledgment of the academic validity of language and religion forecast a probable rise in applied linguistic study in this domain.

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)0.006
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: none
Teacher disagreement score0.871
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
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 integrity0.0000.000
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 head0.012
GPT teacher head0.275
Teacher spread0.262 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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