Developing a Ludonarrative Engine for a Historical Site Using Locative AR and Music: Psychogeography for Battle of the Boyne
Bibliographic record
Abstract
Abstract Mobile digital media technologies, such as smartphones, have great potential for in situ storytelling in cultural heritage sites. By geo-locating narrative content across a site, it can be experienced simultaneously with the physical site, creating a situated narrative of historical relevance. However, looking at and interacting with mobile devices tend to capture the user’s attention and all too easily distract from the site. For this reason, it is important to strike a balance between immersion (into the digital content) and presence (in the physical site). In this paper, we describe an approach to locative storytelling for cultural heritage, which is based on the exclusive use of sound and music for all ludonarrative aspects of the experience and based on the concept of psychogeography, i.e., how places make us feel. Our case study is the Battle of the Boyne in Ireland, the site of the eponymous battle that took place on 1 July 1690 (O. S.) between the forces of the deposed King James II and those of King William III. The battle is considered one of the most significant events in Irish and English history and is part of a wider struggle for power across 17th century Europe. We present our narrative design, sound and music design, game design, which together are captured in our ludonarrative engine in which a combination of historical narrative, music narrative and spatial audio navigation are closely intertwined with game mechanics, creating an immersive experience for cultural, educational and intergenerational engagement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".