MétaCan
Menu
Back to cohort
Record W2296223890 · doi:10.1145/2839462.2839471

Towards a Framework for Tangible Narratives

2016· article· en· W2296223890 on OpenAlexafffund
Daniel Harley, Jean Ho Chu, Jamie Kwan, Ali Mazalek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsToronto Metropolitan University
FundersCanada Research Chairs
KeywordsStorytellingNarrativeComputer sciencePerspective (graphical)Human–computer interactionInteractive storytellingUser interfaceMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a preliminary framework to inform the analysis and design of tangible narratives. Researchers and designers have been using tangible user interfaces (TUIs) for storytelling over the past two decades, but to date no comprehensive analysis of these systems exists. We argue that storytelling systems that use digitally-enhanced physical objects form a unique medium with identifiable narrative characteristics. Our framework isolates these characteristics and focuses on the user's perspective to identify commonalities between existing systems, as well as gaps that can be addressed by new systems. We find that the majority of systems in our sample require the user to perform exploratory actions from an external narrative position. We note that systems that cast the user in other interactive roles are rare but technologically feasible, suggesting that there are many underexplored possibilities for tangible storytelling.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0050.018
Scholarly communication0.0150.020
Open science0.0040.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.023
GPT teacher head0.307
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations78
Published2016
Admission routes2
Has abstractyes

Explore more

Same topicInteractive and Immersive DisplaysFrench-language works237,207