“Dusk can be a magical time in the French Quarter”: Richard Ford’s New Orleans before and after Katrina in “Puppy” and “Leaving for Kenosha”
Bibliographic record
Abstract
Richard Ford has often claimed that he does not consider himself a Southern writer despite being born and raised in Mississippi. Apart from his first two novels, most of his works are set in the North (sometimes the Far North) but, since he lived in New Orleans for some time and knows the city very well, he has devoted two short stories to that singular city. In “Puppy” (2001) and “Leaving for Kenosha” (2008), Ford makes use of various clichés associated with the city; the characters’ behavior is also justified by their living there—space and self being intimately related. Focusing on the description of the city in the two stories, this article points out the gap between the flamboyant city of the past and its present ruins since the 2005 hurricane Katrina. Both stories rely on the dysfunction brought about by an intruder and as the narratives come to an end, some kind of balance has been restored because the life of the city takes over.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".