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Record W4311605308 · doi:10.5430/wjel.v13n1p234

The Sequential Schematic Scene-building Theory in Dan Brown’s The Da Vinci Code: A Cognitive Semantic Study

2022· article· en· W4311605308 on OpenAlexvenueno aff
Mustafa Abdulsahib Abdulkareem, Ahmed Sahib Mubarak

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsSchematicComputer scienceObject (grammar)Motion (physics)Code (set theory)Artificial intelligenceProgramming languageElectrical engineering

Abstract

fetched live from OpenAlex

Everything in the universe is movable or being moved, starting from galaxies and planets to the smallest object, such as the atom. But how can we conceptualize the motion of such entities and what are the basic elements of sequential scene? To do that, a theory of the sequential schematic scene-building has developed to treat this issue. This theory deals with the basic dynamic, on-line, or real-time perceptual processes by which we build a scene. The study focuses on the schematic, not conceptual, elements of sequential scenes which deal with the highly abstracted, primitive system. Such system is considered as the skeleton or building blocks of any sequential scene. To make the theory more applicable, the study selects a scene from Dan Brown’s The Da Vinci Code to be analyzed in terms of the theory developed. The study arrived at a conclusion that the moveable scene can include a group of sequential schematic structures, such as the mover, motion, causality, geometrical structures, and containment. All these elements work together sequentially in the sense that they are inherently consolidated.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.019
GPT teacher head0.311
Teacher spread0.292 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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