Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".