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Record W3096751313 · doi:10.1145/3383668.3419925

Curioscape

2020· article· en· W3096751313 on OpenAlex
Joseph Tu, Ekaterina Durmanova

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicPsychological and Educational Research Studies
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaMitacsUniversity of WaterlooCanada Foundation for Innovation
KeywordsMetagamingVideo game designComputer scienceGame designContext (archaeology)CuriosityGame mechanicsGame art designGame DeveloperSpace (punctuation)Non-cooperative gameGame playHuman–computer interactionSimultaneous gameGame theoryMathematical economicsPsychologyMathematicsSocial psychology

Abstract

fetched live from OpenAlex

Are you frustrated when a board game has too many rules? Do you want to jump straight into the game and just play? We created Curioscape, an escape room board game that focuses on the idea of whether eliminating a rule book is possible in a board game context. This means players can start the game without having to learn rules or understand how the game works. This paper describes Curioscape's conception to release, along with the exploration of replicating escape rooms in a smaller space and investigates if we can use curiosity to create meaningful game design choices.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0360.010

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 head0.265
GPT teacher head0.464
Teacher spread0.200 · 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