Bibliographic record
Abstract
This paper focuses on the triangular links between a text in a given source language, its “translation” into music and an eventual retranslation into another language. As everybody knows, music is a language per se, with all the characteristics of an articulated language, its own syntax, grammar, even its own dialects and “regionalisms.” The bilateral link between a language and music is rather simple and can be summarized in the following principle: when a composer sets a text to music, it is always a one-way-only “translation”; this text cannot and should not eventually be retranslated into another language, there is no going back because music is the most constricting of all languages. Between two “normal” languages, like English or French for instance, solutions can always be found, even deficient ones if necessary, arrangements that are more or less satisfactory, one can compromise, adapt. It is not desirable to translate a text from Chinese into English and then from English into French but it can be done. However, once music has imposed its rules on a text, it becomes the main source language, with which it is impossible to cheat; everything must be literally respected: the musical words and sentences, the general form, the rhythm, the styles, the melodic, harmonic, tonal aspects… There are no possible arrangements or compromises, music comes first and dictates its rules, there are no choices other than to respect, literally, what the music says and hope that it will work or, if it does not, which is most often the case, abandon. And yet in some cases it is necessary to find a way to retranslate the same text. This is when translators are faced with real, at times unsolvable, problems because they are dealing with two source languages, one of which being Music that prevents any continuation to full triangulation. In this article, I will first analyze a few examples to show some of the main difficulties and then propose the solutions that allowed me to solve these problems in a satisfactory fashion.
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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 | 0.030 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".