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Breaking the Frame of Digital, Dream, and Waking Realities

2016· book-chapter· en· W2488600389 on OpenAlexaff
Jayne Gackenbach, Sarkis Hakopdjanian

Bibliographic record

VenueAdvances in psychology, mental health, and behavioral studies (APMHBS) book series · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsMacEwan University
Fundersnot available
KeywordsDreamIllusionPerceptionAestheticsVirtual realityVideo gamePsychologyFrame (networking)PopulationArtSociologyComputer scienceMultimediaCognitive psychologyHuman–computer interaction

Abstract

fetched live from OpenAlex

Just as our dreaming reality is constructed, our waking reality may also be constructed. While our waking reality influences our lives the most, other constructed realities also have impact. Yet, never before has such a large part of the population been so widely affected by another constructed reality beyond dreaming; specifically, our technologically constructed digital reality through video game play. One potential consequence of video game play is breaking the illusion or ‘frame' of our dreams as reality through various dream experiences. Many of the world's wisdom traditions believe that waking reality is an illusion, and now this idea is supported by modern digital physics. While being aware of the illusory nature of waking reality is difficult, it may be easier to break the framework of perception or ‘wake up' to the true nature of reality in alternative realities, such as digital and dreaming. This chapter will review the evidence collected in the video game and dream laboratory to explore how video game play is breaking the frame within dreaming realities.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.009
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.067
GPT teacher head0.443
Teacher spread0.376 · 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
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

Citations2
Published2016
Admission routes1
Has abstractyes

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