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Record W2549867991 · doi:10.1075/tis.11.3.07mer

Language, politics, and the nineteenth-century French–Canadian official translator

2016· article· en· W2549867991 on OpenAlexaboutno aff
Denise Merkle

Bibliographic record

VenueTranslation and Interpreting Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsFrenchPrideNationalismPoliticsLanguage politicsNarrativeHegemonyPoetryHistoryGovernment (linguistics)Political scienceCanadian studiesFrench revolutionMedia studiesCapital (architecture)LiteratureSociologyLawArtAncient historyLinguistics

Abstract

fetched live from OpenAlex

This article aims to contribute to the history of Canadian official translators by looking at three activist translators who were also published writers in post-confederation nineteenth-century Canada. All three francophone official translators “exiled” to Ottawa, the newly designated capital of the young confederation, were actively engaged in creating francophone spaces in and from which they could promote French-Canadian cultures and the French language. Refusing to submit passively to Anglo-dominated government authorship and to the increasingly anglicized Canadian landscape, they coordinated their efforts to carve out a distinct and distinctive place for Canadian francophones. Their weapon of choice in confronting Anglo-Canadian hegemony was authorship. From historical narrative, to novels, caustic songs and nationalist poetry, their writings nurtured pride in the shared history of French-Canadians from different backgrounds — despite the traumatic Grand Dérangement and Conquête — and generated hope for the future of their nation(s).

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)0.000
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 score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.033
GPT teacher head0.282
Teacher spread0.248 · 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
Published2016
Admission routes1
Has abstractyes

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