Bibliographic record
Abstract
Abstract In 2016, a novella titled The Day a Computer Writes a Novel almost won a Japanese literary award. The unusual aspect of the event was that the author was not a human being, but a computer program, trained on 1000 similar short stories and self-instructional writing manuals. The narrative unfolded from the “subjective” viewpoint of the AI itself ending with the following prophetic line: “Writhing in joy unlike any I’d ever felt before, I wrote on, entranced. This was the day a computer wrote a novel. It put the pursuit of its own pleasure first, and ceased serving people.” AI-generated works of literature are everywhere, largely indistinguishable from human-created works. What are the implications of this trend? Are we dealing with a kind of deepfake literature? Or is it truly literature but with a nonhuman authorial creator? If so, “who” or “what” is the author of The Day a Computer Writes a Novel? How do we humans interpret such literature? The repercussions of a possible “takeover” by AI in the domain of literature are enormous. This article looks at these implications, starting with a schematic outline of the advent of AI into this area of traditionally conceived human creativity, followed by a discussion of the relevance of traditional views and concepts related to literature as a meaning-making activity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".