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Record W2007083964 · doi:10.3138/cras-s031-03-04

Norman Mailer’s Harlot’s Ghost: Yet Another Big Book

2001· article· en· W2007083964 on OpenAlexvenueno aff
Barry H. Leeds

Bibliographic record

VenueCanadian Review of American Studies · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature, Film, and Journalism Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDreamLiteratureHistoryReading (process)LawPhilosophyArtPolitical sciencePsychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.197
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.039
GPT teacher head0.267
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2001
Admission routes1
Has abstractyes

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