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Found Objects, Bought Selves

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

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

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.063
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