Bibliographic record
Abstract
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".