Bibliographic record
Abstract
The notion of a musical repertoire's "sound" is frequently evoked in journalism and scholarship, but what parameters comprise such a sound? This question is addressed through a statistically-driven corpus analysis of hip-hop music released during the genre's Golden Age era. The first part of the paper presents a methodology for developing, transcribing, and analyzing a corpus of 100 hip-hop tracks released during the Golden Age. Eight categories of aurally salient musical and production parameters are analyzed: tempo, orchestration and texture, harmony, form, vocal and lyric profiles, global and local production effects, vocal doubling and backing, and loudness and compression. The second part of the paper organizes the analysis data into three trend categories: trends of change (parameters that change over time), trends of prevalence (parameters that remain generally constant across the corpus), and trends of similarity (parameters that are similar from song to song). These trends form a generalized model of the Golden Age hip-hop sound which considers both global (the whole corpus) and local (unique songs within the corpus) contexts. By operationalizing "sound" as the sum of musical and production parameters, aspects of popular music that are resistant to traditional music-analytical methods can be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".