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Record W4205835868 · doi:10.1163/22105956-12341327

Introduction: Sufi Texts in Translation

2021· article· en· W4205835868 on OpenAlexaff
Amer Latif, Alexandre Papas, Mohammed Rustom

Bibliographic record

VenueJournal of Sufi Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Linguistic Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsVariety (cybernetics)LinguisticsInterpretation (philosophy)Translation studiesDiversity (politics)Translation (biology)Computer scienceHistoryLiteratureSociologyPhilosophyArtificial intelligenceArtAnthropologyBiology

Abstract

fetched live from OpenAlex

Abstract This special issue of the Journal of Sufi Studies attempts to make the case that the act of translation is best seen as a recurrent activity necessitated by the various changes that inform the interpretation of Sufi texts on the one hand, and the languages and cultures that receive them on the other. The nine Sufi texts featured in this collection illustrate the diversity of genres and variety of languages in which they have been written, thereby pointing up the distinctive hermeneutic potentials of translation theory and practice when these texts are rendered by experts into modern European languages, particularly English and French.

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)0.003
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0050.006
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0230.004

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 head0.082
GPT teacher head0.370
Teacher spread0.288 · 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
GenreEditorial

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
Published2021
Admission routes1
Has abstractyes

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