Musical Robots and the Serbian Kolo: A Narrative Analysis of Ana Sokolović’s Géométrie sentimentale (1997)
Bibliographic record
Abstract
Composer Ana Sokolović divided her instrumental work Géométrie sentimentale into large sections inspired by pure geometric shapes — Triangle, Cercle and Carré — describing these sections as three contrasting perspectives of the same musical materials. This article uses a narrative analytical approach as a lens through which to understand these distinct sections and the materials populating them. Inspired by Sokolović’s employment of musical objects in her compositions and by the extra-musical concepts inspiring many of her works, this analysis uses a collection of colourful robot toys as metaphors for the work’s materials. Three unique perspectives of these toys are described: in Triangle, the robots interact as characters on a dramatic stage; in Cercle, they peacefully coexist in slow motion; and in Carré new combinations of robot elements are abruptly juxtaposed against each other. The characteristics and interactions between these toys, as well as the various harmonic ‘masks’ that the composer has them wear, are helpful in understanding Sokolović’s harmonic structure, variation/transformation techniques, formal organization and rhythmic characteristics. The Serbian kolo is also shown as influential on the work, relating directly to the physicality and kinetics of the metaphorical robots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".