Joaquín Rodrigo's Con Antonio Machado : a performer's guide to the work, focusing on the analysis of songcyclic features
Bibliographic record
Abstract
In spite of the increasing interest in Spanish song in North America, this music is still under represented in the standard vocal repertoire. In an attempt to shed light on a work lying outside the small, accepted canon of Spanish songs and collections, this dissertation focuses on Con Antonio Machado by Joaquín Rodrigo. Con Antonio Machado raises the interesting question of overall coherence. In the preface to the work, the composer refers to it as a song collection. However, Rodrigo was commissioned to write a song cycle, and indeed his starting point was searching for a poetic cycle in Antonio Machado's works. Although Rodrigo did not find such a group of poems, in the preface to the work he acknowledges the existence of recurrent themes in Machado's poetry. Therefore, it seems fair to suggest that he might have approached the collection in the spirit of writing a cycle. Aiming to answer the question of coherence within the work, this dissertation is an in-depth study of Con Antonio Machado, focusing on the analysis of song-cyclic features. Its purpose is to serve as a basis for making well-informed performance decisions. The dissertation begins with a review of literature that deals with the notion of song cycle (Chapter 1). Chapter 2 focuses on the questions of whether, to what extent and in what ways the poetry of Antonio Machado plays a role in the coherence of the work. The chapter includes a brief reference to Machado's aesthetic ideals and discusses some of the recurrent themes and symbols in his poetry. Chapter 3 concentrates on the musical coherence of the work. It identifies the existence of a large-scale harmonic plan and of a network of recurring melodic gestures. Chapter 4 discusses Con Antonio Machado on a song-by-song basis. It places each song in the context of the entire work, but also accounts for its particularities and tries to say something about its poetical, musical, and emotional essence. Finally, the Conclusion integrates and summarizes the findings of the previous chapters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".