Peer Review #3 of "French validation of the Barcelona Music Reward Questionnaire (v0.1)"
Bibliographic record
Abstract
Background.The Barcelona Music Reward Questionnaire (BMRQ) questionnaire investigates the main facets of music experience that could explain the variance observed in how people experience reward associated with music.Currently, only English and Spanish versions of this questionnaire are available.The objective of this study is to validate a French version of the BMRQ.Methods.The original BMRQ was translated and adapted into an international French version.The questionnaire was then administered through an online survey aimed at adults aged over 18 years, fluent in French.Statistical analyses were performed and compared to the original English and Spanish version for validation purposes.Results.A total of 1027 participants completed the questionnaire.Most responses were obtained from France (89.4%).Analyses revealed that congruence values between the rotated loading matrix and the ideal loading matrix ranged between 0.88 and 0.96.Factor reliabilities of subscales (i.e., Musical Seeking, Emotion Evocation, Mood Regulation, Social Reward and Sensory-Motor) also ranged between 0.88 and 0.96.In addition, reliability of the overall factor score (i.e., Music reward) was 0.91.Finally, the internal consistency of the overall scale was 0.85.The factorial structure obtained in the French translation was similar to that of the original Spanish and English samples. Conclusion.The French version of the BMRQ appears valid and reliable.Potential applications of the BMRQ include its use as a valuable tool in music reward and emotion research, whether in healthy individuals or in patients suffering from a wide variety of cognitive, neurologic and auditory disorders.
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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 | 0.024 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.195 | 0.089 |
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".