Breaking the Silence on How Visitors and Educators Perceive Quiet Spaces in Museums
Bibliographic record
Abstract
In recent years, museums have begun to think more about the value of creating ‘quiet spaces’. However, there remains little systematic discussion in the literature regarding their definition, characteristics, and purpose. This exploratory study examined perceptions of quiet spaces in museums from the standpoint of both museum educators and visitors. Through semi-structured interviews with 40 participants, it was determined that educators and visitors similarly perceive museum quiet spaces as restorative, inclusive, varied, situated, and spaces to process emotions. Furthermore, the outcomes demonstrated that the meaning of quiet spaces are contextual and mediated by the interplay between the individual’s context and the environmental characteristics of the quiet space itself. These spaces hold the potential to afford enjoyable, memorable, and even transformative experience that supports learning and are, at times, a necessary part of the museum experience for many visitors. The study also speaks to the potential of quiet spaces to be integral to psychological restoration within the museum experience for individuals to exercise their visitor agency and well-being. This study provides valuable insights about the importance and significance of these often neglected or unconsidered museum spaces for visitors and educators alike.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".