Chapter 1 The Digital Situation of Poetry
Bibliographic record
Abstract
The Digital Situation of PoetryPoetry in the twenty-first century finds itself in a media-technological situation that is both new and not new.On the one hand, it is not new because technology and media have always been part of poetry's environment.On the other, the situation is new because digital technology and media have configured novel frameworks and situations for the production, distribution and reading of poetry.Poetry books might contain programming languages.Using computer code, Mez Breeze, for example, has created a new language, "mezangelle."Algorithms write poetic texts, as in the case of the poet Johannes Heldén and Håkan Jonson's Evolution (2014) or Karen ann Donnachie and Andy Simionato's The Library of Nonhuman Books (2019), an autonomous art installation where artificial intelligence is programmed to create new books from old publications.Jason Edward Lewis produces visual and tactile poetry as apps to be read on a single mobile medium, preferably with a touch screen, in his series of poems called P.o.E.M.M.: "Speak," "Know," "Migration," "Bastard," "Choice," "White" and "Death" (2007)(2008)(2009)(2010)(2011)(2012)(2013).Poetry travels between media with highly different technological and institutional affordances, i.e. between books, computers, theater scenes, performances and installation rooms.These new technological conditions of poetry have had a direct effect on poets' and readers' everyday lives as well as on literary institutions.In Canada and the USA, the career of Rupi Kaur has been a powerful illustration of such changes.Her success demonstrates the ways in which social media intervenes with the field of poetry.The story is well known: Kaur established herself as a popular poet on Instagram, with an overwhelming number of readers.Following this online success, she made her debut with the collection of poems Milk and Honey (2015), topping the New York Times bestseller list.This instant success would have been unlikely without Instagram and other influential social media.Similarly, social media has played an important role for new voices such as R.M. Drake, Atticus, Nayyirah Waheed, Lang Leav, Yrsa Daley-Ward and, though on a smaller scale in terms of followers and financial success, Sabina Store-Ashkari, Alexander Fallo and Trygve Skaug in Norway.Furthermore, in the digital era, sound poetry and poetry readings have expanded their fields of distribution and sites for performance, becoming at once more visible and audible.This phenomenon, in turn, has had an impact on poetry's appearance in physical rooms, on stages and in public and semi-public places.These shifts have not only been limited to poetry slams but have blossomed into widespread practices through which young and emerging poets have developed their own styles, as in the cases of Maren Kames, Amanda Gorman and Olivia
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 | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".