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Record W3168530700 · doi:10.26156/om.2021.13.1.005

My Memories of Ivan Wyschnegradsky

2021· article· ru· W3168530700 on OpenAlexaboutno aff
М. Брюс

Bibliographic record

VenueOPERA MUSICOLOGICA · 2021
Typearticle
Languageru
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMemoirStyle (visual arts)Value (mathematics)LiteraturePersonalityPeriod (music)ArtArt historyHistoryPsychoanalysisPsychologyAesthetics

Abstract

fetched live from OpenAlex

Воспоминания канадского композитора Брюса Матера представляют собой уникальное свидетельство о последних годах жизни и творчества выдающегося русского композитора Ивана Вышнеградского (1893–1979), одного из основоположников европейской микротоновой музыки. Под пером Матера личность Вышнеградского предстает во всей своей полноте: как композитора, теоретика, педагога, подвижника. Особую ценность тексту воспоминаний придают письма Вышнеградского, в которых запечатлен стиль речи и мышления композитора. Одно[1]временно Матер короткими, но точными штрихами передает дух эпохи и нюансы художественной жизни Франции и Канады 1970-х годов. На русском языке «Воспоминания» Брюса Матера публикуются впервые. “My memories of Ivan Wyschnegradsky” by Canadian composer Bruce Mather is one of the unique testimonies about the last years of life and work (1974–1978) of the outstanding Russian composer, one of the founders of European microtonal music. Under the pen of Mather, Wyschnegradsky’s personality appears in its entirety: as a composer, theorist, teacher, and ascetic. Wyschnegradsky’s letters, which capture the composer’s style of speech and thinking, add special value to the text of his memoirs. At the same time, Mather conveys the spirit of the time and the nuances of the artistic life of France and Canada in the 1970s in short but precise strokes. In Russian, “Memoirs” by Bruce Mather is published for the first time.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.051
GPT teacher head0.321
Teacher spread0.270 · 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.

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
Published2021
Admission routes1
Has abstractyes

Explore more

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