Faulkner's County: The Historical Roots of Yoknapatawpha , and: Faulkner, Mississippi (review)
Bibliographic record
Abstract
This essay reviews Don H. Doyle's Faulkner's County: The Historical Roots of Yoknapatawpha and Edouard Glissant's Faulkner Mississippi . Doyle's book is a detailed social history of Lafayette County, the county in Northern Mississippi on which Faulkner's fictional Yoknapatawpha was based, and I argue that he illustrates the ways in which fiction and history can assist each other in the search for truth. Glissant's book is more dense and philosophical, and I offer an interpretation of his claim that Faulkner's fiction works through "deferred revelation," a literary process that is inseparable from social and cultural Creolization. I argue that Glissant's reading of Faulkner suggests possible ways in which to re-vision literary modernism. Together, the two books underscore the historical and philosophical significance and value of Faulkner's work. Le présent essai porte sur Faulkner's County: The Historical Roots of Yoknapatawpha , de Don H. Doyle, et Faulkner, Mississippi , d'Édouard Glissant. L'ouvrage de Doyle est l'histoire sociale détaillée du comté de Lafayette, ce comté du nord du Mississippi sur lequel Faulkner a basé son roman Yoknapatawpha, et, à mon avis, il illustre comment la fiction et l'histoire peuvent s'associer dans la recherche de la vérité. Celui de Glissant est plus dense et philosophique, et je fais valoir que les romans de Faulkner passent par une « révélation différée », un processus littéraire inséparable de la créolisation sociale et culturelle. Selon moi, cette évaluation de Faulkner par Glissant suggère comment on pourrait revoir le modernisme littéraire. Ensemble, ces deux ouvrages viennent souligner la signification philosophique et historique ainsi que la valeur du travail de Faulkner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".