What is This Music? Musical Taste, Psychology, Sociology, Media, and Manipulation
Bibliographic record
Abstract
Why do people like the music they like-and hate what they hate?Musical taste is connected to identity, personality, and community, as well as to marketing, semiotics, and technology.This thesis explores the many facets of musical tastefrom the history of the recording business, to the way music taste is framed in the media, to the body of research on musical preference and personality, to the way our tastes are measured (and shaped) in the era of big data-and how these threads are connected.Using extensive research into the study of music preference, as well as interviews with some of the leading researchers in the field, the thesis sheds light on the psychological and social factors that underpin our musical identities.I first want to thank Tracey Lindeman, a treasured colleague from the trenches of alternative media, who suggested that I pursue these studies at Carleton.During my studies, I took three elective classes in the Music and Culture program.These classes opened my mind to many of the ideas circulating in current discourse about music and society.I want to thank my professors:
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".