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Record W4233247828 · doi:10.25071/1925-5624.40366

Tracing the Local: The Translator-Travellee in French Accounts of India

2018· article· en· W4233247828 on OpenAlexvenueno aff
Sanjukta Banerjee

Bibliographic record

VenueTusaaji A Translation Review · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVernacularSanskritIndian subcontinentContext (archaeology)LinguisticsTypologyNasalizationRepresentation (politics)HistorySociologyPolitical sciencePoliticsEthnologyPhilosophyAnthropology

Abstract

fetched live from OpenAlex

This paper examines aspects of multilingual India as described in a few eighteenth-century French travel accounts of the subcontinent to underscore the interactional history of representation that the conventions of European travel writing have tended to elide, particularly in the context of the subcontinent. It draws on the notions of fractal and vertical in travel to examine vernacular-Sanskrit relations encountered by the travellers, and to render visible the role of the “translator-travellee” in embedding vernacular knowledge in international discursive networks. Rather than merely questioning the travellers’ often skewed and necessarily partial readings of India’s linguistic plurality, I approach these travel accounts as crucial for understanding the specificity of the region’s multilingualism, one that was largely incommensurable with the typology of language that the accounts seek to establish.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.095
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0080.015
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.273
Teacher spread0.231 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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Same venueTusaaji A Translation ReviewSame topicHistorical Linguistics and Language StudiesFrench-language works237,207