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Record W635276826

Stan Lee : conversations

2007· book· en· W635276826 on OpenAlexaboutno aff
Jeff McLaughlin

Bibliographic record

VenueUniversity Press of Mississippi eBooks · 2007
Typebook
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsComicsAdventureUncannyArtArt historyPerformance artPublishingPopular cultureMainstreamMedia studiesPoliticsLiteratureVisual artsSociologyLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Stan Lee (b. 1922), cocreator of the Amazing Spider-Man, the Fantastic Four, the Incredible Hulk, and the Uncanny X-Men, is one of the most successful writers and publishers of comics. During the 1960s and 1970s, he wrote superhero adventures for Marvel Comics. His storylines imbued the genre with angst and contemporary politics and focused as much on the personal lives of his characters as on heroics. His work, in collaboration with cartoonists such as Jack Kirby and Steve Ditko, remains deeply influential. His role as a spokesperson and impresario for Marvel paved the way for the superhero genre to be taken seriously by the critical establishment and for the penetration of Marvel Comics into mainstream American culture. Stan Lee: Conversations collects interviews ranging from 1968 to 2005. Lee's charm, good humor, and keen business sense are on display. He has spirited conversations with cartoonists Jack Kirby, Harvey Kurtzman, and Roy Thomas, talk show host Dick Cavett, and Jenette Kahn (head of DC Comics, Marvel's rival), among others. He talks with candor about his creative process, publishing, film and television adaptations of his comic books, and the evolution of the comics industry. The volume concludes with a new interview conducted by the editor. Jeff McLaughlin is assistant professor of philosophy at Thompson Rivers University in Kamloops, British Columbia.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.214
Threshold uncertainty score0.717

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0110.002
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.2140.119

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 head0.034
GPT teacher head0.196
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2007
Admission routes1
Has abstractyes

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