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Record W2922191516 · doi:10.3138/ctr.178.009

Acting and Motion Capture in a Digital Gaming World

2019· article· en· W2922191516 on OpenAlexvenueaboutno aff
Mark Lipton, J. R. Tanner

Bibliographic record

VenueCanadian Theatre Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMonetizationHollywoodWork (physics)Digital RevolutionVideo gamePoliticsDigital mediaMultimediaAdvertisingEngineeringComputer sciencePolitical scienceBusinessWorld Wide WebArtTelecommunicationsEconomics

Abstract

fetched live from OpenAlex

The digital game industry refers to the economic sector involved in the development, marketing, and monetization of video games. The rising use of digital platforms has turned video games into a massively profitable sector. Digital games are a new frontier in our global culture, with many players aiming to find some gold by partaking in the industry. In particular, actors are exploring this terrain as a possible arena in which to make their mark. This summer, Mark Lipton sat down with actor Joy Tanner to discuss and debate today’s digital gaming world. Together, they considered changes to the requirements of actors and actor training, the state of acting for digital games, and the politics of digital gaming in Canada, and particularly between Hollywood and Silicon Valley; finally, they pull back the shroud of mystery to reveal the working conditions many actors in the gaming world work under.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.853

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)0.0000.000

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 head0.298
Teacher spread0.272 · 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 designNot applicable
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

Citations0
Published2019
Admission routes2
Has abstractyes

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