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Found Objects, Bought Selves

2015· book-chapter· en· W2499947691 on OpenAlexaff
Lynne Heller

Bibliographic record

VenueAdvances in social networking and online communities book series · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsAvatarObject (grammar)Representation (politics)Mode (computer interface)Process (computing)AestheticsPoliticsComputer scienceSociologyArtHuman–computer interactionMultimediaPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter traces a process of creating using found object collage, through collecting/consuming practices and finally to the notion of the bought self, avatar representation through consumerist artistic practice in Second Life (SL) the online, user generated, virtual environment. Positioning collage as a reinvigorated current in art, the text couples this mode of making with shopping as found object. Collaboration is inherent in an online virtual world, where programmers, designers and other content providers determine the parameters of what is possible. Found object/shopping is a synergistic fit with the nature of predetermined boundaries coupled with late-stage capitalism. This mode of self-making encourages the idea of buying identification through the construction of an avatar. Through a review of the practices of the Situationists, an aesthetic turn in political tactics is revealed through contemporary art making. The text uses the author's own virtual/material practice as a case study for the theories explored.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0080.007
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.063
GPT teacher head0.353
Teacher spread0.289 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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