Bibliographic record
Abstract
In 1959, Norman Mailer aired an ambition in Advertisements for Myself which he must have known and intended that the literary world would never let him forget: to “try to hit the longest ball ever to go up into the accelerated hurricane air of our American letters” (477). Since then, Mailer has written many books, several of them massive in size and scope, which his detractors have rejected as failed attempts to fulfill this promise. Writing in the New York Times Book Review on The Executioner’s Song, Joan Didion described this critical phenomenon best when she forcefully insisted: It is one of those testimonies to the tenacity of self-regard in the literary life that large numbers of people remain persuaded that Norman Mailer is no better than their reading of him. They condescend to him, they dismiss his most original work in favour of the more literal and predictable rhythms of The Armies of the Night; they regard The Naked and the Dead as a promise later broken and every book since as a quick turn for his creditors, a stalling action, a spangled substitute, tarted up to deceive, for the “big book” he cannot write. In fact, he has written this “big book” at least three times now. He wrote it the first time in 1955 with The Deer Park and he wrote it a second time in 1965 with An American Dream and he wrote it a third time in 1967 with Why Are We in Vietnam? and now, with The Executioner’s Song, he has probably written it a fourth. (Didion)
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".