Agencies in Feminist Translator Studies: Barbara Godard's Agency at the Crossroads of Literature in Canada
Bibliographic record
Abstract
This book sets out a new framework for a feminist history of translators, drawing on the legacy of Canadian scholar Barbara Godard and her work in establishing the Canadian literary landscape as a means of exploring agency in feminist translation studies and its implications for cross-disciplinary debates. The volume is organized in three sections, establishing feminist translator studies as its own approach, examining these dynamics at work in a comprehensive portrait of Barbara Godard’s scholarly and literary history, and looking ahead to future directions. In situating the discussion on Godard and Canadian literary history, Elena Castellano calls attention to a geographic context in which translation and its practice has been at the heart of debates around national identity and intersected with the rise of feminism and feminist literary scholarship. The book demonstrates how an in-depth exploration of the agency of an individual stakeholder, whose activities spanned diverse communities and oft conflicting interests, can engage in key questions at the intersection of nation-making, translation, and feminism, paving the way for future research and the further development of feminist translator studies as methodological framework. This book will be of interest to scholars in translation studies, feminist literature, cultural history, and Canadian literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.062 | 0.033 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".