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Record W639992634

Conversations with William Gibson

2014· book· en· W639992634 on OpenAlexaboutno aff
Patrick Smith

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2014
Typebook
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsFuturistCyberspaceMedia studiesReading (process)NewspaperAlienationVariety (cybernetics)Social mediaHistoryLiteratureSociologyArtArt historyThe InternetPolitical scienceLawWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

After reading Neuromancer for the first time, literary scholar Larry McCaffery wrote, knew I had seen the future of [science fiction] (and maybe of literature in general), and its name was William Gibson. McCaffery was right. Gibson's 1984 debut is one of the most celebrated SF novels of the last half century, and in a career spanning more than three decades, the American Canadian science fiction writer and reluctant futurist responsible for introducing cyberspace into the lexicon has published nine other novels. Editor Patrick A. Smith draws the twenty-three interviews in this collection from a variety of media and sources--print and online journals and fanzines, academic journals, newspapers, blogs, and podcasts. Myriad topics include Gibson's childhood in the American South and his early adulthood in Canada, with travel in Europe; his chafing against the traditional SF mold, the origins of cyberspace, and the unintended consequences (for both the author and society) of changing the way we think about technology; the writing process and the reader's role in a new kind of fiction. Gibson (b. 1948) takes on branding and fashion, celebrity culture, social networking, the post-9/11 world, future uses of technology, and the isolation and alienation engendered by new ways of solving old problems. The conversations also provide overviews of his novels, short fiction, and nonfiction.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.196
Teacher spread0.172 · 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
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

Citations6
Published2014
Admission routes1
Has abstractyes

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