A Comparison of the Multiple Intelligence Profiles of Trainee Music Teachers in Respect of Music Genre Preference
Bibliographic record
Abstract
The aim of this study was to compare the multiple intelligence profiles of students in the Music Education Department of Necmettin Erbakan University, Ahmet Kelesoglu Faculty of Education, Department of Music Teaching of Fine Arts Department, in relation to the variable of music genre to which they preferred to listen. The data collection tool used in the research was the 80-item “Scale for the Evaluation of Multiple Intelligence Areas”, developed by Armstrong, and to obtain the relevant variables, a structured interview form was prepared. The scale was applied to 106 trainee music teachers. Conformity of the data obtained to normal distribution was assessed with the Kolmogorov-Smirnov test, and in the comparisons of multiple groups, the One Way ANOVA test was applied as the data showed normal distribution. The most general result that emerged was that there was a difference between the points of the multiple intelligence profiles that the students have developed according to the music genre to which they listen. A striking result was that there was a significant difference between all the intelligence profile points of the students who preferred to listen to rap/hip-hop music and those of the students who preferred other music types.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".