A case study of the top 100 favoured songs on Apple Music platform among Malaysians based on their song track attributes / Haziq Nazhan Hamdan
Bibliographic record
Abstract
The purpose of this study was to investigate the top 100 favored songs on the Apple Music platform among Malaysians, based on their song track attributes. This study used secondary and quantitative data to analyze the popularity and song track attributes of the top 100 songs in Malaysia on the Apple Music platform. The results of this study show that the most popular songs in Malaysia are primarily influenced by American, British, Korean, Indonesian, and Canadian music, with American music being the most favored among Malaysians. Additionally, the study found that the most preferred music genre among Malaysians is Pop music, accounting for 84% of listeners on the Apple Music platform. Based on these findings, it is recommended that Malaysian artists aim to incorporate elements from popular music styles, such as high danceability and energetic music features, in their own music to increase their appeal and popularity among the local audience. This study contributes to the field of music analysis by providing valuable insights into the music preferences of Malaysians on the Apple Music platform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".