Beats from the East: Transcultural Adaptation of Hip-Hop from North America to Eastern Asia
Bibliographic record
Abstract
Hip-hop culture has spread from its origins in the South Bronx in the late 1970’s to many countries across the globe, leading to the creation of a Global Hip-hop Nation in which artist from every nation have the ability to share, collaborate, and critique the works of others. With the expansion of a culture that was created – predominately – by marginalized African-Americans, an issue that has arisen is the authenticity of what constitutes ‘real’ Hip-hop for those outside of North America. In this paper the author will explore the relationship between Hip-hop cultures in North America and Eastern Asia in an attempt to show how both cultures have influenced each other rather than the commonly held view that Eastern Asian countries (Japan, China, and South Korea) have copied and imitated the styles of North America. Through secondary research and exploring the scholarship surrounding the creation and expansion of Hip-hop culture the author will examine the circumstances that have lead to the popularity Hip-hop has gained in North America and Eastern Asia, and examine the aspects of Eastern Asian culture that have influenced Hip-hop artists in North America to show that the imitation and adaptation of cultures works both ways.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| 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".