Bibliographic record
Abstract
William Faulkner was one of the best-known American authors of the twentieth century. Experimenting with form, chronology, and language, Faulkner developed a strikingly personal style while exploring the complexities of life in the American South. He was especially interested in crafting stories that explored the effects of the Civil War’s destruction and the ways in which it revealed the breakdown of plantation-based aristocracy, the effects of the exaggerated chivalric code of the Old South, and the complex racism of a society once based on slavery. He is most famous for novels such as Absalom, Absalom!, Light in August, The Sound and the Fury, Sanctuary, and As I Lay Dying. Many of his novels are set in fictional Yoknapatahpha County, a county of his own design that resembled his own birthplace, Lafayette County. A native of Oxford, Mississippi, Faulkner lived most of his life there; he also joined the Canadian Air Force during World War I and spent time in Hollywood later in his career writing screenplays. He struggled with alcoholism throughout his life, but eventually died from a heart attack following a fall from his horse.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".