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Record W4320723247 · doi:10.32920/22094789

Gwendolyn Moore: The ‘Ordinary’ Translator as Cultural Intermediary

2023· preprint· en· W4320723247 on OpenAlexaffabout
Ruth Panofsky

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsThe artsQueen (butterfly)Formative assessmentSociologyGovernment (linguistics)Media studiesArt historyManagementLawHistoryPolitical sciencePhilosophyPedagogyLinguistics

Abstract

fetched live from OpenAlex

This essay draws on archival materials in the Harvest House fonds housed at Queen’s University (Kingston, Ontario) to recover and demystify the nature of Gwendolyn Moore’s formative work in French‑to‑English translation in the years 1970 to 1973. The essay responds to Jeremy Munday’s call to attend to the “ordinary” translator who did not gain prominence but whose work was nonetheless integral to the cultural fabric of her society. In focusing on Moore’s connection with Harvest House publisher Maynard Gertler and studying her role as the trailblazing translator of Yves Thériault and Anne Hébert, the essay argues that she became a key intermediary of cultural exchange in the early 1970s, when the Canadian government was yet in the process of formalizing a program of arts translation grants under the aegis of the Canada Council for the Arts. In essence, before translators had acquired professional standing within literary Canada, Moore conducted herself as a professional.

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)0.009
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: Other
Teacher disagreement score0.091
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.019
Scholarly communication0.0080.008
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.142
GPT teacher head0.331
Teacher spread0.189 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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