Bibliographic record
Abstract
Faulkner’s Absalom, Absalom! is the story of Thomas Sutpen, whose rise and fall mark the book’s action. The reader must come to terms with the tragedy of Sutpen which is written in a convoluted sequence of time and events told by four narrators, the most significant of which is Quentin Compson. In a letter to his publisher, Faulkner justified choosing Quentin as a focalizer for the text: “... I use his bitterness which he has projected on the South in the form of hatred of it and its people to get more out of the story.”(qtd in Williamson, 244). As Quentin and his Canadian roommate, Shreve, sit in their room at Harvard smoking pipes, Quentin relays the story of Sutpen’s abandonment of his family and subsequent trip to the West Indies. Sutpen takes a job as an overseer on a plantation, and during his employment there is a rebellion. This uprising lasts several days, and on the eighth day the plantation runs out of water and something has to be done. Quentin tells Shreve: “so he [Sutpen] put the musket down and went out and subdued them. That was how he told it: he went out and subdued them ” (Faulkner 204). He quelled this rebellion and came back to marry the Planter’s daughter. This decision marks not only his rise to power, it marks also the tragic choice that ends up defining his life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.243 | 0.157 |
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".