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From Line to Design: A Reading of Derek Beaulieu’s Digital Poetry

2022· article· ar· W4303647651 on OpenAlexaboutno aff
Asmaa Magdy Moawad

Bibliographic record

Venueمجلة بحوث کلية الآداب جامعة المنوفية · 2022
Typearticle
Languagear
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryReading (process)ArtLine (geometry)LiteraturePhilosophyLinguisticsMathematics

Abstract

fetched live from OpenAlex

In the process of electronic communication, even in its most aesthetic and poetic aspects, the identity of both of the sender and the recipient seems to be completely diminished. In its veiling of essential human data such as gender, nationality, ethnicity…etc, e-poetics seems to break the conventional author-reader connection. It does not permit much, if any, presupposed agreements upon common human and aesthetic values. Such “e-distribution of thought, or e-philosophy” (Beaulieu 2002) (6), as Beaulieu terms it, seems to devoid conventional author-reader agreement upon, at least, one common aspect such as time, space, context, emotions, or any mutual human concerns. The question that comes to mind here is: does not such deliberate dissociation of actual author-reader connection, both explicit and implicit, create other hidden forms of e-associations? Does not such a staunch attack upon the egoistic presence of both of reader and author open the back door for another collective ego that, at least virtually, shares the same aesthetic interests of claimed omnipotence presence? This paper, therefore, will attempt to answer these questions in the light of the work of most recent Canadian e-poet, critic and publisher; Derek Beaulieu.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.132
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0090.037
Scholarly communication0.0080.006
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.076
GPT teacher head0.248
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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