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Record W3127204434 · doi:10.5539/ass.v17n2p109

Second Life: A Hope for Ideal, But Only in Its Imaginary

2021· article· en· W3127204434 on OpenAlexvenueno aff
Xiaowei Huang

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicViolence, Religion, and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual worldArt worldAestheticsFictional universeIdeal (ethics)The ImaginaryExtension (predicate logic)Identity (music)MetaverseSociologyPossible worldCorporationNarrativeComputer scienceVirtual realityEpistemologyPsychologyArtHuman–computer interactionLawPolitical sciencePhilosophyArt historyPsychoanalysisPerformance artLiterature

Abstract

fetched live from OpenAlex

Second Life, one of the most popular of virtual world was invented by Linden Lab Corporation: its philosophical statement is that Second Life is 'a place where you can turn the pictures in your head into a kind of pixelated reality’ (Rymaszewski, 2007, p. iv). Second Life is, for many reasons to be considered in this essay, a representative example of the (offline/real) world. This paper will argue that virtual worlds such as Second Life are an extension of the real world. Two major questions were posed earlier in this essay: ‘Does the virtual world represent the real world?’ and ‘Is it a refuge for its participants from the real world’. We can answer these questions. First of all, the virtual world is an extension of the real world, because it is built from, and continues to make use of, ideas, meanings, identity categories, performances, narratives and values derived from the real world. Secondly, Second Life is clearly not, in the long term, a refuge for its participants from the real world. Second Life can be understood as a virtual place that allows people to have temporary fun; however, it is only for a moment or for a short period, because the relationship between the virtual and the real world is interpenetrated.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.759

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.270
Teacher spread0.243 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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