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Record W4252591090 · doi:10.14318/hau3.3.006

Animating interaction

2013· article· en· W4252591090 on OpenAlexaff
Paul Manning, Ilana Gershon

Bibliographic record

VenueHau Journal of Ethnographic Theory · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsTrent University
Fundersnot available
KeywordsTrope (literature)AnimationSocial mediaComputer scienceMetaverseHuman–computer interactionAestheticsSociologyVirtual realityArtWorld Wide WebComputer graphics (images)Literature

Abstract

fetched live from OpenAlex

This article engages with Ten Silvio's 2010 challenge to treat animation as a central trope for understanding the relationship between selves and media. We discuss how animation can illuminate aspects of interactions that performance, the current dominant trope, can not—such as addressing what it means to be human when what distinguishes the human from the nonhuman is how one is being controlled. We discuss how focusing on animation can shed light on social interactions both in virtual worlds and with media practices in which online and offline exchanges are inextricably intertwined.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.005
Scholarly communication0.0040.005
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0270.003

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 head0.025
GPT teacher head0.316
Teacher spread0.291 · 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 designQualitative
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

Citations64
Published2013
Admission routes1
Has abstractyes

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