Locative Narrative: Creating Contexts for Supposition in Spatially Distributed Museums
Bibliographic record
Abstract
Audio guides and games have long been staple modes of interpretation in museums. The medium of locative narrative, defined here as participatory site-specific story experiences that are heard on headphones, offers alternative modes of engagement with archives and collections where the visitor becomes a participant in an unfolding drama [1]. Museums become the site of arts interventions, taking collections ‘outside of heritage’. Informed by cognitive research in auditory perception [2], [3], and building on locative media research in outdoor environments [4], [5], The Lost Index: NATMUS (2015) [6], situated in Copenhagen at The National Museum of Denmark and the DieselHouse museum, explores how participants can experience story across spatially distributed locations. The narrative is communicated in a series of phone calls from different characters across the sites. Techniques to focus attention and verbal suggestion [7] are used to influence interpretation of the environment. The science-fiction genre invites participants to engage in imagining and offers a rationale for the perceptual transformation of the buildings and the city. Sound is a key aspect to linking the locations. Compositions in binaural audio simulate the aural qualities of the fictional places, plotted temporally and spatially within the museum rooms. Movement responsive sound is triggered by participants’ own smartphones via the novel use of Bluetooth ‘iBeacons’. Participants, in the role of protagonist, move simultaneously within the story’s locations and museum. The confluence of the existent world and narrative representations is an often-reported feature of “mixed reality” [8] experiences [9]. The auditory dimensions can appear to subtlety change, affecting interpretation of sounds as live, recorded or imagined. While there are many factors that may affect a participant’s response to spatially distributed narrative across museums, an approach is put forward here for creating contexts for supposition. References [1] Whittaker, E. (2015), ‘Inside the snow globe: Pragmatisms, belief and the ambiguous objectivity of the imaginary’, Technoetic Arts: A Journal of Speculative Research, 13: 3, pp. 275–284 [2] Denham, S. & Winkler, I. (2015) ‘Auditory perceptual organization’. In. Wagemans, J. The Oxford Handbook of Perceptual Organisation. Oxford: Oxford University Press, p. 601 [3] Moore, B. C. J. (2012) Introduction to the Psychology of Hearing. 6th Edition. Bingley: Emerald Group Publishing [4] Benford, S. Crabtree, A. Reeves, S. et al (2006) The Frame of the Game: Blurring the Boundary between Fiction and Reality in Mobile Experiences. CHI 2006, April 22–27, 2006, Montréal, Québec, Canada. [5] Reid, J. (2008) ‘Design for Coincidence: Incorporating Real World Artefacts in Location Based Games’. DIMEA’08, Athens, September 10–12. [6] Whittaker, E. & Brocklehurst, J. R. (2015) ‘The Lost Index: NATMUS’ [iOS Application]. Apple Inc. [https://itunes.apple.com/us/app/the-lost-index-natmus/id1058419473?mt=8] [7] Weitzenhoffer, A. M., Higard, E. R. Stanford Hypnotic Susceptibility Scale: Forms C Modified by John F. Kihlstrom. Palo Alto: Stanford University Press (1999) [8] Milgram, P. & Kishino, F. (1994). ‘Taxonomy of Mixed Reality Visual Displays’. IEICE Transactions on Information and Systems. Vol. E77-D, No.12 December 1994. [9] Montola, M., Stenros, J. & Waern, A. (2010) Pervasive Games, Theory & Design. Burlington: Morgan Kaufmann
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".