Embodied Engagement with Narrative: A Design Framework for Presenting Cultural Heritage Artifacts
Bibliographic record
Abstract
An increasing number of museum exhibits incorporate multi-modal technologies and interactions; yet these media divert visitors’ attention away from the cultural heritage artifacts on display. This paper proposes an overarching conceptual structure for designing tangible and embodied narrative interaction with cultural heritage artifacts within a museum exhibit so that visitors can interact with them to comprehend their cultural context. The Tangible and Embodied Narrative Framework (TENF) consists of three spectra (diegetic vs. non-diegetic, internal vs. external, and ontological vs. exploratory) and, considering how different interactions map along these three spectra, can guide designers in the way they integrate digital media, narrative, and embodiment. In this paper, we examine interactive narrative scholarship, existing frameworks for tangible and embodied interactions, and tangible and embodied narrative projects. We then describe the design of the TENF and its application to the pilot project, Mapping Place, and to the case study project, Multi-Sensory Prayer Nuts. The findings indicate that embodied engagement with artifacts through a narrative role can help visitors (1) contextualize the meaning of artifacts and (2) make personalized connections to the artifacts. Based on this work, we suggest design recommendations for tailoring the use of the TENF in the cultural heritage domain: simulate cultural practices, associate visitors with cultural perspectives, and provide simultaneous digital feedback. We conclude by describing future directions for the research, which include generating other possible projects using the TENF; collaborating with other designers and museum professionals; and exploring applications of the TENF in museum spaces.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".