Can Near Death Narratives, Ontologies and Language Analysis with Natural Language Processing Help Us to Understand the Quantum Mind?
Bibliographic record
Abstract
Altered states of consciousness (ASC) encompass phenomena such as near-death experiences (NDEs). NDEs are concise accounts of individuals who have undergone clinical death and subsequently been spontaneously resuscitated or revived, retaining recollection of their experiences during that interval. Numerous individuals who have undergone near-death experiences have recounted experiencing intense mental lucidity, extraordinary sensory images, and a distinct recollection of the event that surpasses the realism of their ordinary existence. The Quantum Hologram Theory of Physics and Consciousness (QHTC) elucidates the fundamental characteristics of our existence and the quantum properties of the human mind. QHTC proposes that the brain functions in a manner akin to a hologram, adhering to quantum principles. The QHTC proposes that during an ASC, cognitive processes accelerate and there is an enhanced level of perceptual lucidity. Natural language processing (NLP) refers to a collection of computer methods used to analyze and represent texts that occur naturally. Ontology is a firmly established theoretical field in the philosophy of language that focuses on conceptual frameworks for understanding reality. This study employs NLP to extract linguistic sequences from NDEs narratives stored in a database including 4267 records. It then utilizes ontology research approaches to establish a mapping between the QHTC ontology and human language. The research aims to verify some ontological components of the QHTC, including the notion that during ASC, cognitive processes accelerate and there is an enhanced level of perceptual lucidity.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".