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Record W2963050214 · doi:10.1386/public.30.59.72_1

Animal Eyes: Gazing at the Animal in Video Games

2019· article· en· W2963050214 on OpenAlex

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.

Bibliographic record

VenuePublic · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAnthropocentrismObjectificationRealismSubjectivityGazeVideo gamePanopticonAestheticsPsychologySociologyEnvironmental ethicsEpistemologyArtComputer sciencePhilosophyPsychoanalysisMultimedia

Abstract

fetched live from OpenAlex

Encounters with animals are common in video games, where they are often included to add realism to the gameworld. Encounters with animal subjectivity, however, are not. The anthropocentric nature of video games means that animals are often environmental objects, and sometimes resources, but only occasionally characters, and rarely protagonists. As a consequence, there is no encounter with the animal presence, and often no shared gaze: The look of the animal is instead transformed into something wholly antagonistic (such as in Horizon Zero Dawn), wholly submissive (as in Far Cry Primal) or absent entirely (Red Dead Redemption). Even when video game animals are designed with the intention of “appreciating” the animals, the privileging of their panoptic human spectators just as often results in their objectification.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.031
GPT teacher head0.319
Teacher spread0.288 · 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