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Record W4226193494

Two Artists, Two Portraits: Cohen/Joyce – A Study in Affinity

2022· article· en· W4226193494 on OpenAlexaboutno aff
Nigel Hunter

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicModernist Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsPortraitArtArt historyVisual artsCombinatoricsMathematics
DOInot available

Abstract

fetched live from OpenAlex

Canadian singer-songwriter Leonard Cohen (1934-2016) was a poet and novelist before becoming world-famous as part of the 1960s and ‘70s counterculture. His two novels, The Favourite Game (1963) and Beautiful Losers (1966), are significant contributions to Canadian literature and to postmodern fiction in general. Cohen himself, and more than one contemporary commentator, claimed for them certain affinities with the work of James Joyce, and the present account reflects on this claim. What in the progress of Cohen’s protagonist Lawrence Breavman, of The Favourite Game, echoes the education in consciousness of Joyce’s Stephen Dedalus? Religion, politics, and sexuality are emphatic presences in both narratives; art too, clearly. But, is Joyce’s A Portrait of the Artist as a Young Man (1916) simply a template for later autobiographical Künstlerromane, or is it Joyce’s example as an original master of the form that may be more pertinent here? Where are the main points of convergence and divergence between these two artists and their fictions? An attempt to elucidate some answers may contribute to the construction of an early consensus regarding Cohen’s literary status – and to the question of Joyce’s ongoing importance to later generations and later phases of artistic and cultural production.

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)0.002
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: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0490.030
Scholarly communication0.0140.007
Open science0.0020.005
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.280
GPT teacher head0.533
Teacher spread0.252 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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