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Record W2885309173 · doi:10.4000/13zrr

Les affinités musicales du prince de Ligne

2017· article· fr· W2885309173 on OpenAlexaff
Marie Cornaz

Bibliographic record

VenueÉtudes sur le XVIIIe siècle · 2017
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Comme tout aristocrate de son rang, Ligne a dû recevoir une formation musicale, mais il reste muet à ce propos dans ses Fragments de l’histoire de ma vie, écrit autobiographique dans lequel on ne trouve guère que quelques références à des mascarades musicales auxquelles il a participé, jeune garçon, vers 1750, au Théâtre de la Monnaie, dans le cénacle de la cour du gouverneur Charles de Lorraine. Dans les archives du château de Belœil, on trouve des traces de paiement pour des leçons de danse données au jeune prince dès l’âge de 4 ans. Tout au long de sa vie, son goût musical évolue. Finalement, il apparaît que le prince, qui se méfie des personnes qui n’ont pas la fibre musicale, est éclectique en musique, aimant tout autant l’opéra comique que la tragédie gluckiste ou les oratorios de Haydn. S’il pleure en écoutant l’air « Ô Richard, ô mon roi » de Richard Cœur-de-Lion de Grétry, ses affinités le portent surtout vers l’innovation opératique du classicisme viennois. Bien qu’ouvert à une certaine modernité, le prince semble pourtant ignorer un pan important de celle-ci, la musique instrumentale, quasi absente de ses écrits.

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)0.001
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.007
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.053
GPT teacher head0.249
Teacher spread0.196 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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