Reminiscence bump invariance with respect to genre, age, and country
Bibliographic record
Abstract
We report a cross-cultural study investigating musical reminiscence bumps, the phenomenon whereby adults remain emotionally invested in the music they preferentially listened to in adolescence. Using a crowdsourcing service, 4,824 participants from 102 countries were each required to recall five songs (titles and artist names), resulting in a 24,120-song study. In addition, participants provided demographic information and answered questions relating to the songs they recalled, such as age first listened to, levels of nostalgia, and associated emotions. Song titles and artist names were cleaned and genre information established through fuzzy matching recalled information to songs within an open-source music encyclopedia. These data, plus participants' demographic information, allowed reminiscence bumps differentiated by age, sex, country, and genre preference to be explored. Recency-bias effects of recalled songs were also investigated. Results demonstrated that the musical reminiscence bump phenomenon is common to all age groups and both sexes, pervasive across all countries, and is not restricted to particular genres. In sum, musical reminiscence bumps appear to be biologically and culturally ubiquitous.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".