Flaubertian Aesthetics, Modernist Ethics and Animal Representation in Hemingway's <i>Green Hills of Africa</i>
Bibliographic record
Abstract
Abstract Hemingway's Green Hills of Africa is widely deplored for its portrayal of trophy hunting. Acknowledging the accusations, I question their ethical conclusions by drawing on recent work in narrative ethics in order to consider how animals are treated not only in the narrative but also by the narrative. Animals uniquely test the ethics of representation; to address this aesthetic and ethical challenge, Hemingway appropriates techniques adaptable from Flaubert's principled commitment to the mot juste and authorial impersonality. The result is a highly mimetic, often ineloquent style that complicates any simple negative reading of his stance toward animals. Attending to Hemingway's stylistic practices opens his writing to more nuanced interpretations of his narrative ethics; it also helps situate his work with other modernist attempts to represent animals as beings inaccessible to human ways of thinking, yet not beyond ethical consideration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".