Bibliographic record
Abstract
The Postscript asks whether a machine could in the future successfully imitate a human poet. It discusses the history of artificially generated poems from early schoolroom manuals through John Clark’s ‘Eureka Machine’ of 1845 to the age of the computer. It relates Alan Turing’s ‘Imitation Game’, in which a computer mimics the linguistic behaviour of a human being, to a wider mid-twentieth-century tendency to see poetry as the ultimate challenge for an electronic imitator of human behaviour. The chapter argues that a computer which depended on statistical modelling of prior poetic corpuses would not be able to replicate the actions of a human imitator, because imitating authors imitate not simply words but practices, and those are not simply codifiable. Imitators do not simply follow the rules implicit in earlier texts, but might imitate an earlier author’s willingness to break those rules. The chapter shows that a pervasive opposition between biological and digital systems runs through writing about the possibility of artificially imitating human consciousness, which is the latest manifestation of the opposition between a ‘living’ recreation of a past author and a simulacrum. It concludes by discussing the Xenotext by the Canadian experimental poet Christian Bök, which seeks to create a perpetually living poetry engine embedded in the DNA of a permanently durable microbe. This takes the long-standing metaphor of a ‘living’ imitation to the cellular level, and makes of imitatio an unending biological process of transformation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.190 | 0.053 |
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".