Bibliographic record
Abstract
From Frederic Henry and Catherine Barkley in Ernest Hemingway's A Farewell to Arms (1929) to Lloyd Gruver and Hana-ogi in James A. Michener's Sayonara (1953), the trope of romantic relationships between American servicemen and the foreign women whom they encounter overseas has recurred throughout twentieth-century American literature. In this article, I examine the ideological assumptions rooted in these depictions, paying particular attention to the challenges that the Iraq War, an apparently “sexless” conflict, has posed to contemporary fiction writers. Using a close reading of Phil Klay's short story “In Vietnam they had Whores” (2014) and Atticus Lish's novel Preparation for the Next Life (2015), I argue that these two authors have “pivoted” away from Iraq towards Asia in ways that reveal an increased willingness on the part of war writers to critique or dispense with old narrative stereotypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".