From Poetry As the Excess of Language to Poems Generated by Artificial Intelligence (Poet As Researcher in the World of New Cultural Paradigms)
Bibliographic record
Abstract
Beuys' expanded concept of the artwork is also relevant in poetry, where we can see the expansion of poetry into other fields and new media; poems can be found in the sky, in space, on the skin of performers, on facades, in the sand, in the snow and in the digital medium, where we also encounter AI-generated poetry.Poetry is contextualised, integrated into the social, alongside esoteric searches towards minimalist texts that can be read by machines or disappear in the process of being read, we encounter poetry in social media and as excellent content in prime time TV shows (Million's poet competition in the United Arab Emirates, from 2007 to the present).In this text we are interested in poetry as research, complementary to research in other fields, which means that we can also understand poetry in terms of cognitive activity and the poet as cognitive worker.We also pay attention to experimental explorations in the temporary poetry, which conflict with the tendency to situate such researches in the printed book.AI-generated texts are something other than poetry understood as an excess of language, the work of a corporeal poet with emotions, experiences and passions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".