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Record W2072438722 · doi:10.1145/2793107.2793135

Operation Citadel

2015· article· en· W2072438722 on OpenAlexafffund
Daniel Yule, Bonnie MacKay, Derek Reilly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCasualNarrativeInterpretation (philosophy)Computer scienceSet (abstract data type)IntermediaryGame designHuman–computer interactionPolitical scienceBusinessMarketing

Abstract

fetched live from OpenAlex

In this paper we describe the role of docents in a mixed reality game at a historic site called Operation: Citadel. The docents act as intermediaries between the system and the participants, providing interpretation and understanding of the game and managing interaction. This permits the integration of sophisticated interactions and rich narrative while maintaining the walk-up-and-use, casual nature of the exhibit. We describe the implementation and design, and examine the effect that docents had on enjoyment of and frustration with the game. Our results indicate that docents can serve an important role in augmenting participant experience. We identify and describe a set of roles unique to docents of Mixed Reality experiences.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.159
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1590.022

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 head0.093
GPT teacher head0.300
Teacher spread0.206 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2015
Admission routes2
Has abstractyes

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Same topicVirtual Reality Applications and ImpactsFrench-language works237,207