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Record W947802178 · doi:10.5206/notabene.v8i1.6597

Less is More, and More is Less, More or Less: The Historical Progression, Aesthetic Characteristics, and Physical Limitations of Minimalism

2015· article· en· W947802178 on OpenAlexaffvenue
Sam Boer

Bibliographic record

VenueNota bene · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsThe King's University
Fundersnot available
KeywordsMinimalism (technical communication)AestheticsJazzArtRelation (database)Repetition (rhetorical device)Order (exchange)MusicalVisual artsLiteraturePhilosophyLinguisticsComputer science

Abstract

fetched live from OpenAlex

Since its emergence as an aesthetic category in the mid-twentieth century, minimalism has been contentious amongst scholars of all forms of art. It has been alternately celebrated, questioned, and condemned by not only its critics, but also the artists whose works have been given the historical title “minimalist.” This article explores the emergence of minimalist music, examining its relation to the earlier “avant-garde” works of John Cage and other eclectic influences, such as jazz and Eastern music. In doing so, this article attempts to establish a broad understanding of the elements integral to minimalist music, with a special focus on the composers La Monte Young, Terry Riley, and Steve Reich. The works of Riley and Reich are compared to the works of visual artists Barnett Newman and Sol LeWitt in order to highlight the pivotal elements of the minimalist aesthetic, including repetition, simplicity, and, to borrow Cage’s term, “Unfixity.” This article concludes that the minimalist compositions of the aforementioned composers ultimately demonstrate the integral characteristics of minimalism better than their visual counterparts, due to the temporal nature of music. However, the article seeks to demonstrate the importance of contemplating visual and musical interpretations of minimalism together, as they are complimentary windows into this modern movement.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.148
GPT teacher head0.281
Teacher spread0.133 · 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 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

Citations1
Published2015
Admission routes2
Has abstractyes

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