” Clarinet Praxis” : How to best prepare for orchestra life. Advice and interviews with some of the main principal clarinetists in Europe.
Bibliographic record
Abstract
This topic for the present thesis arose from the very reason I came to study at the KMH Royal College of Music of Stockholm where I have the opportunity to approach orchestral life in a direct manner, playing and collaborating with the great orchestras of the city and also to focus on orchestral repertoire, especially those excerpts which are required for auditions. In the last year I have spent a lot of time trying to understand how an orchestral musician can manage time better and prepare for any kind of situation, precisely because I have had so many chances to participate in major projects that the university has offered and also in long tours around Europe, Canada and Central America. The theme of the present thesis, therefore, is what how I find the life in the orchestras I’ve worked with. Tours show directly that playing and travelling for long periods of time can interfere with what we call the "physical form of the instrumentalist". The same thing happens when working for weeks on different productions, especially when you still must study and learn what may be a huge orchestral repertoire. An orchestra can be a place for meeting and learning, but in the long run you need to carve out a personal study space to better prepare for the sometimes-frenetic life of a musician. My own experience and the reports of other musicians have shown how complicated it is to make all the commitments coincide; in fact, thanks to this unusual research on a subject rarely discussed formally I have learned directly and indirectly how to organize successful the personal way of practice. In the research carried out, questions were asked to understand how orchestral musicians prepare some orchestral passages. I wanted to put attention to specific details, for example, sound control, the articulation of the various technical passages. I’ve chosen where possible regarding excerpts that not only have advanced my own experience but are also very important in the clarinet repertoire. In addition, I also wanted to talk about how important (and often essential) it is to have a flexible way of playing both in rehearsals and in concert. In presenting this research, my intention is also to discuss some facts from my personal experience as an orchestral musician and answers to many questions I have prepared for my teachers and perhaps future orchestral colleagues. The aim of this research is produce a report that is of use for any musician: one who already has worked in an orchestra or is still doing auditions, but also for teachers and students entering this environment, so as to allow everyone to explore and better understand this aspect of a musician's life. This has been a very interesting undertaking for me, and I believe an important one as it follows one of many paths, we musicians all take to reach the same goal.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".