The impact of producers’ comments and musicians’ self-evaluation on perceived recording quality
Bibliographic record
Abstract
The choice of recording technologies always transforms musicians’ perception of their performance when playing in the studio. In many cases, during recording sessions, musicians repeat the same musical composition over and over again without the presence of an audience. We hypothesize that comments from an external record producer and/or self-evaluation after listening to the takes in the control room address the challenges of studio recording by helping musicians improve from one recorded take to another. We conduct a field experiment with 25 jazz players, grouped into five ensembles, participating in recording sessions with four record producers. The musicians are invited to record four compositions, one in each of four experimental conditions. To create these conditions, we independently manipulate two types of feedback between takes: with or without comments from the record producer and with or without musicians’ self-evaluation (after listening to the takes in the control room). Our results show that both external comments and self-evaluation provide objectivity by giving the ensemble a common ground. Specifically, listening to the first take enhances creativity while external comments positively impact a takes’ evolution throughout the session
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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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".