SpaceScript: An Improvisational Method for Exploring Older Adults’ Lived Spatial Experience in Participatory Research
Bibliographic record
Abstract
Understanding how different individuals experience and adapt everyday spaces is important for spatial design to become more inclusive. Unfortunately, in their work, spatial designers (e.g., architects) often refer to normative data about human behavior that exclude or stereotype those who fall outside the norm. There is a need for approaches capable of capturing human behavior within everyday contexts in greater depth. This paper introduces SpaceScript , a qualitative research method that combines theatre-based improvisation with real-time interaction to access the lived, embodied, and relational dimensions of spatial experience. It offers an agency-centered approach, enabling participants to co-create scenarios, adjust environmental elements, and explore comfort strategies through active, improvisational play. Grounded in participatory research, improvisational practice, and embodied inquiry, SpaceScript uses scaffolded, scenario-based exercises that prompt movement, adaptation, and reflection. Rather than relying on recall or fixed tasks, the method generates insights through active participation, revealing how comfort, accessibility, and social dynamics are shaped through memory, experimentation, and interaction. This paper details our development of SpaceScript and presents findings from applying it with older adults, a population often underserved and stereotyped in research and design. SpaceScript actively engaged participants in trial-and-error discovery, spatial problem-solving, and collaborative meaning-making. The method surfaced subtle dynamics, such as the contrast between perceived and actual comfort, the creative reinterpretation of spatial objects, and the ways participants constructed meaning through memory, experimentation, and dialogue. Beyond data collection, SpaceScript fostered social connection by offering participants a playful, collaborative space that encouraged interaction and reduced isolation. SpaceScript adds a participatory and embodied approach to qualitative spatial inquiry, one that captures not only how different people navigate space, but also how they construct meaning, solve problems, and engage socially through their environments.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".