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The Reality of the World and the Multidimensionality of the Reality of the Individual: The Semantic Meaning of Architecture of Consciousness

2020· article· en· W3107193561 on OpenAlexvenueno aff
Svitlana Kalishchuk

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2020
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Philosophical Inquiry
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)ConsciousnessArchitecturePsychologyCognitive scienceEpistemologyPhilosophyArtVisual arts

Abstract

fetched live from OpenAlex

In psychology, the world's multidimensionality is manifested in the study of consciousness, in identifying the methodological foundations of psychology and psychology of consciousness. The structure of the multidimensional world of a man is described from the point of view of the subject's understanding of the characteristic features of the realities where man lives. Aims of study are to formulate and consider the problem of the world's reality and the multidimensionality of the reality of the individual in the perspective of the semantic architecture of consciousness and within the framework of a holistic model of representation of the psychology of understanding the human world. The pragmatic aim of the present research is to set a quantitative measure in which a person is drawn into virtual reality (TER). The meta-analysis of publications with quantitative empirical data reporting on the results of staff training using virtual and augmented reality. The criteria of the search were that articles must be published exceptionally in peer-reviewed journals and be issued not older than in 2017. The platform for the effective search was the Google Scholar search engine. The transfer efficiency factor (TER) was the key element of the analysis. All gathered data extracted from the relevant literature sources were interpreted through observation, description, quantitative calculation, complex analysis, methods of comparison, generalization, and abstraction method. Although learning conditions with more virtual immersion lead to slightly worse results than real learning conditions, most people show similar results after learning, regardless of the level of virtual immersion. The inhomogeneity of acceptance by individuals of virtual reality is found out in quantitative data of the considered studies. The average score for the performance of each training condition represented in the scientific works considered for this study, as well as the 95% confidence interval, was calculated. It was found out that the value of TER 0.5 turned into a percentage by multiplication by 100%, indicates that training on the simulator can reduce the time of personal training by half. Presence is a psychological phenomenon that occurs in the human mind, not in specific technologies. The usage of virtual reality in learning affects the sense of presence and immersion. It directly affects an individual's perception of world reality. At the same time, the individual consciously accepts the picture of simulated reality, which emphasizes the multidimensionality of the individual's reality.

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)0.008
metaresearch head score (Gemma)0.015
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 score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.008
Science and technology studies0.0020.034
Scholarly communication0.0100.019
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.108
GPT teacher head0.321
Teacher spread0.214 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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