The Sequential Schematic Scene-building Theory in Dan Brown’s The Da Vinci Code: A Cognitive Semantic Study
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".