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Record W2087101510 · doi:10.5539/ass.v4n11p138

Vision Logic and Aesthetic Evolution

2009· article· en· W2087101510 on OpenAlexvenueno aff
Feng Zhu

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
Fundersnot available
KeywordsApperceptionTasteUnconsciousnessPsychologyConsciousnessAestheticsProcess (computing)Meaning (existential)Cognitive scienceCognitive psychologyComputer sciencePhilosophyEpistemologyNeuroscience

Abstract

fetched live from OpenAlex

Visual logic is the most basic logic principle that human beings’ aesthetic taste is based on. Through conducting search on aesthetic taste, we found out the common rule for aesthetic evolution, which is also an evolution process in a sense of meaning. Firstly, visual system apperceives aesthetic objects in a mode similar to computer searches. In the process of apperception, visual system is controlled by “collective unconsciousness” standard formed through the deposition of “collective” experience and by “individual unconsciousness” formed through the accumulation of “individual” experience. The brain clears up the messages through visual logic, and makes deduction, after which the brain could obtain accurate visual apperception. Under proper circumstances, aesthetic resonance is generated. Further, the aesthetic taste of the group could be sublimed, which in turn exerts impact on the “unconsciousness” aesthetic standard of individuals and collective groups. This is the basic way for the evolution of aesthetic taste and is the evolution logic which occurs momently.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.016
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.338
Teacher spread0.318 · 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 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

Citations1
Published2009
Admission routes1
Has abstractyes

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