“I come back to stand here, a revenant”: Brian Moore’s “Returned Yank” Travel Writing
Bibliographic record
Abstract
In line with the special issue’s commitment to elucidating some of Brian Moore’s less well-known works, this article focuses on a significant set of his nonfiction essays that are explicitly framed as “travel writing,” particularly those that appeared in the U.S. magazines Holiday and Travel & Leisure. It takes seriously Michael Cronin’s assertion that the literature of migration and diaspora has overshadowed Irish travel writing in scholarly commentaries, with the potential danger that “only certain forms of movement are privileged in analysis”: “The permanent move to Canada but not the sojourn in Sicily, the emigrants’ letters home from Australia but not the visit to Berlin, become objects of critical inquiry.” Considering Moore’s travel writing about Ireland in particular, the article argues that Moore adopts a highly self-conscious “Returned Yank” persona to negotiate some of the tensions of travel writing itself. Focusing on three prominent spaces in Moore’s Irish travel writing – Belfast, the West of Ireland and the Anglo-Irish “Big House” – the article explores the ways in which Moore fashions a “travel writing” self that is just as fictitious as any of the characters in his novels, enabling him to simultaneously meet the expectations of his U.S. editors and readers and satisfy his own urge to inform, educate and subtly critique some of the clichés of travel writing about Ireland.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
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".