Validation of the Measure of Emotions by Music (MEM)
Bibliographic record
Abstract
If music connects to our most resonant emotional strings, why have not we used it to assess emotions? Our goal was to develop and validate the Measure of Emotions by Music (MEM). A total of 280 participants were randomly assigned to MEM Condition 1 (excerpts) or MEM Condition 2 (excerpts and adjectives). All participants responded to the PANAS-X. The internal consistency (α) of the MEM subscales (Happy, Sad, Scary, Peaceful) was in acceptable-to-strong range and similar to the PANAS-X. Construct validity of the MEM illustrated cohesive convergence to the PANAS-X. Confirmatory factor analysis confirmed the validity of a four-factor solution, as intended in the MEM. Split-half reliability shows good fidelity. A total of 69% of the respondents mentioned a preference for the MEM. The MEM demonstrates very good psychometric characteristics, seems to be an appreciated way to measure emotional states and may represent an interesting alternative for clinical groups having difficulties to identify their emotion with words.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".