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Record W4407891323 · doi:10.1163/9789004695566_010

Representing Interpreters in Theater and History in Seventeenth-Century New France

2025· book-chapter· en· W4407891323 on OpenAlexaboutno aff
Xing Wu

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHistorical Influence and Diplomacy
Canadian institutionsnot available
Fundersnot available
KeywordsInterpreterHistoryArtVisual artsArt historyComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Interpreters were integral to translingual encounters in the early modern period, but their portrayal in literature and history has received limited scholarly attention. This chapter explores the innovative representation of a forest spirit as a theatrical interpreter in a play performed by schoolboys of French descent at the Jesuit College in Québec in 1658. This réception play, staged to welcome the colonial governor of New France, transcended the translingual scenario depicted in Marc Lescarbot’s Le Théâtre de Neptune (1606), the earliest recorded theatrical production in colonial North America. In the 1658 play, the theatrical interpreter dramatized multilingualism and the process of oral translation. It drew upon the knowledge production of Indigenous languages, signaling the presence of interpreters from diverse ethnic backgrounds in New France. The chapter then compares the plays with contemporaneous historical accounts, particularly Lescarbot’s Histoire de la Nouvelle-France and the Jesuit Relations, which described French-Indigenous interpreters. This comparison further illuminates how translingual communications were presented to Francophone audiences in the colony and back in France and helps us understand the various ways in which real-life interpreters mediated French-Indigenous encounters.

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: Empirical · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score0.487

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.019
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.291
Teacher spread0.269 · 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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Same topicHistorical Influence and DiplomacyFrench-language works237,207