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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; both teacher heads agree on what is shown here.
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".