The geography of music preferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considerable attention has been paid to America’s political and economic divides. These divides revolve around class and location, with more affluent, more educated and denser places leaning more open-minded and liberal and less affluent, less educated and less dense places leaning more conservative. We contend that such divides are also reflected and reinforced by preferences, attitudes and predispositions for culture. More specifically we argue that Americans’ preferences for music will reflect dimensions of these political and economic divides. To test this proposition, our research examines the geographic variation of five key categories of music preferences across 95 of the largest US metropolitan areas. We use factor analysis to identify and map geographic variation of musical preferences, and we use both bivariate correlation analyses and regression analysis to examine the associations between metro-level musical preferences and key economic, demographic, political, and psychological variables. We find that musical preferences generally reflect and reinforce America’s broader economic and political divides.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it