The Harribance Effect as Pervasive Out-of-Body Experiences: NeuroQuantal Evidence with More Precise Measurements
Bibliographic record
Abstract
A historical summary is presented of the published and unpublished measurements of the physical changes associated with Sean Harribance while he accurately discerned recondite information about others. Reports of his subjective experiences as well as the quantitative electroencephalographic (QEEG), sLORETA, SPECT, and neuropsychological data support the occurrence of measurable and specific out-of-body like states that are strongly correlated with independent ratings of his accuracy of people he does not know but with whom he is spatially proximal. Coherence between theta (Schumann resonance) and gamma activity within the right temporoparietal and frontal regions and his left temporal region is congruent with information acquired by his right hemisphere and interpreted by the left. The quantitative values of the emissions of photons and alterations in the intensity of the geomagnetic field (both equivalent to energies of about 10-11 J/s or 10 pW/m2) around his head during this state were congruent with the estimated numbers of neurons responsible for the QEEG coherence and are strongly indicative of neuroquantological processes that allow an interface between extracerebral energies, neuronal
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".