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Record W4248694465 · doi:10.22215/etd/2016-11654

An Exploration of In-Game Action Mappings with a Deformable Game Controller

2016· dissertation· en· W4248694465 on OpenAlexaff
Paden Shorey

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsCarleton University
Fundersnot available
KeywordsGestureCategorizationComputer scienceAction (physics)Controller (irrigation)Binary numberScheme (mathematics)Simple (philosophy)Artificial intelligenceHuman–computer interactionComputer visionMathematicsPhysics

Abstract

fetched live from OpenAlex

Deformation input consists of methods of interaction that make use of material deformation in order to control on-screen variables. In this thesis, we explore controller input mappings for games using a deformable prototype that combines deformation gestures with standard button input. In experiment one, we tested discrete gestures using three games. We categorized the control schemes as binary (traditional), action, and navigation, the latter two named based on the game mechanics the gestures were mapped to. We found that the binary scheme performed the best, but gesture-based control schemes are stimulating and appealing.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.680
Threshold uncertainty score0.529

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 communication0.0000.005
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.297
Teacher spread0.271 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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