To Go or Not to Go: Exploring Gen Z's Attitudes toward Museum Visiting
Bibliographic record
Abstract
The goal of this research is to explore the reasons why young people, in particular the Generation Z (Gen Z) cohort, visit or do not visit publicly funded museums.To remain vital, museums of all kinds (including art museums/galleries) need to continue to attract an audience.Much research has been done on the attitudes of older generational cohorts toward museum visiting, but it has not fully understood the attitudes of this younger, more tech-savvy generation.Survey and interview data were collected from undergraduate students (Gen Z members) to begin to understand their attitudes toward and motivation for museum visiting or not visiting.Three traditional goals for museums, that is, education, social cohesion and entertainment, were used as a lens to gain insight into how Gen Z views museum visiting.The results of the survey confirmed Gen Z's recognition of the museum's function in education and entertainment; the museum's function in strengthening social cohesion was not fully confirmed.The interview data provided insight into participants' perceptions of why they felt that museums were not currently doing enough to attract visitors of their age group.Participants also offered suggestions for areas where the museum could improve in the future and described their expectations.Recommendations that will help museums attract more Gen Z visitors are made.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".