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LETTERS ON THE PAGE AND ON THE SCREEN: CONCRETE POETRY AND “MOVIES OF WORDS” BASED ON THE EXAMPLE OF THE POEM BY BPNICHOL

2024· article· en· W4391918039 on OpenAlexaboutno aff
Анна Швец

Bibliographic record

VenueLomonosov Journal of Philology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryLiteratureArt

Abstract

fetched live from OpenAlex

The article discusses two texts by the Canadian poet Barry Phillip Nichol (known under the pseudonym bpNichol): the poem Evening’s Ritual from 1967, printed in the collection of visual poems Konfessions of an Elizabethan Fan Dancer (1967), and the poem Letter from 1984, included in the digital work First Screening (1984). The 1967 text belongs to the tradition of concrete poetry, while the 1984 text is closer to the art of cinema and electronic literature of the 20th and 21st centuries. Importantly, the 1984 text attempts to translate the 1967 text into the digital realm, as the content of the texts is identical. The researcher highlights the difference in the performative realizations of the same statement. The author notes that in the case of the poems Evening’s Ritual and Letter from the collection First Screening, the intended meaning can be identified not so much with the proposition as with the performative effect produced on the recipient. In other words, the meaning is realized primarily in the realm of the pragmatics of artistic communication rather than semantics. By exploring the performative effect, the author suggests finding it in the structure of “scriptural imagination” (J. McGann), the preconception of the experience of writing, and notes the difference in configurations of imagination in both texts. While the printed text of Evening’s Ritual allows the reader to be a co-author of the statement, the digital text of Letter excludes the reader from the process of meaning-making.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.220
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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