Bibliographic record
Abstract
Summary : Norms for a musical corpus. In this article, we present norms for a standardized set of 144 melodie excerpts. The melodies have been standardized on four variables corresponding to familiarity, verbal evocations, musical categories (vocal or instrumental) and age of acquisition. For this purpose, estimations were obtained in 120 French university students. The results show that musical excerpts can be submitted to psychometric measures similar to normative measures obtained for other stimuli. Except for the age of acquisition which was only determined for a limited number of excepts, the other variables such as familiarity, musical category as well as verbal responses associated to each musical except were identified. The present results extend data obtained in a previous investigation with the same set of melodies infrench speaking subjectsfrom Quebec by Peretz et her colleagues (1995). This study provides a standardized set of melodies available for research in the psychology of music. Key words : norms, music, familiarity, verbal associations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".