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Transatlantic Women's Literature

2008· book· en· W1489414713 on OpenAlexaboutno aff
Heidi Slettedahl Macpherson

Bibliographic record

VenueEdinburgh University Press eBooks · 2008
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryPolitical science

Abstract

fetched live from OpenAlex

This book contributes to the evolving debate surrounding Transatlantic Studies and transatlantic literature. It focuses on twentieth-century women's narratives of travel and adventure, and their deliberate expansion of the Transatlantic concept beyond the familiar US–UK axis to include Canada, South America, the Caribbean and Eastern Europe. The crisscrossing of the Atlantic is contested and problematised throughout. The book explores culturally resonant literature that imagines ‘views from both sides’ and examines the imaginary, ‘in-between’ space of the Atlantic. It offers a considered exploration of the way in which the space of the Atlantic and women's space work together in the construction of meaning in transatlantic texts. Focusing on contemporary literature, the book engages with a range of texts, from novellas and novels to essays, memoirs and travel literature. Nella Larsen's Quicksand is read alongside Bharati Mukherjee's Jasmine in relation to constructions of the exotic; Eva Hoffman's Lost in Translation is explored in relation to travel memoirs such as Jenny Diski's Skating to Antarctica and Stranger on a Train; and Anne Tyler's transatlantic novel The Accidental Tourist is read alongside her latest transpacific novel, Digging to America, and Isabel Allende's Daughter of Fortune. Readers will gain an appreciation of the complexity of transatlantic narratives and the ways in which they are defined by, and infused with, gender considerations.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.698
Threshold uncertainty score0.600

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0150.013
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.011
GPT teacher head0.181
Teacher spread0.170 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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Same venueEdinburgh University Press eBooksSame topicCanadian Identity and HistoryFrench-language works237,207