The phenomenon of nostalgia in American popular music. The cases of Drake’s “Honestly, Nevermind”, Beyoncé’s “Renaissance”, and The Weeknd’s “Dawn FM”
Bibliographic record
Abstract
Ubiquitous music has become an integral part of our lives. Sounds from the United States and Canada have almost always dominated the charts and the hearts of audiences around the world. This is not surprising since these top-quality productions hit people's tastes very easily, enjoying particular popularity. Among the multitude of musical genres, performers and songs, it is also easy to highlight the numerous elements that have common references in all musical genres. One of these is the creation of nostalgia in music, which, among other things, manifests itself through the use of various musical motifs or distinctive sounds or instruments to create music similar to a particular time period. Artists also reach back to old songs so as to create an entirely new work from them, but this is just one way of referring to the past in creativity. A perfect example of this issue is the albums “Dawn FM”, “Renaissance” and “Honestly, Nevermind”, released in 2022, which all show, through their similarities and differences that this trend is being used as much as possible by the biggest names in American popular music today.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".