Patterns of social inequality in arts and cultural participation: Findings from a nationally representative sample of adults living in the United Kingdom of Great Britain and Northern Ireland.
Bibliographic record
Abstract
Context: A significant amount of literature indicates the health benefits of arts engagement. However, as this engagement is socially patterned, differential access to and participation in the arts may contribute to social and health inequalities. Objective: This study aimed to uncover the patterns of participation in arts activities and engagement with culture and heritage among adults in the United Kingdom of Great Britain and Northern Ireland, and to examine whether such patterns are associated with demographic and socioeconomic characteristics. Methodology: We applied latent class analysis to data on arts and cultural participation among 30 695 people in the Understanding Society study. Multinomial logistic regression was used to identify predictors for the patterns of activity engagement. Results: For arts participation, adults were clustered into "engaged omnivores," "visual and literary arts," "performing arts" and "disengaged." For cultural engagement, adults were clustered into "frequently engaged," "infrequently engaged" and "rarely engaged." Regression analysis showed that the patterns of arts activity were structured by demographic and socioeconomic factors. Conclusion: This study reveals a social gradient in arts and cultural engagement. Given the health benefits of arts engagement, this suggests the importance of promoting equal access to arts and cultural programmes, to ensure that unequal engagement does not exacerbate health inequalities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".