Correlations between onsite and global networks of random number generators during group healing meditations
Bibliographic record
Abstract
INTRODUCTION: The Global Consciousness Project 2.0 (GCP 2.0) is an initiative investigating the relationship between human consciousness and the behavior of the project's random number generators (RNGs). This was explored via a series of meditation workshops led by Dr. Joe Dispenza with between 1000-2200 participants. They are run almost monthly and include meditative practices and 3 separate Coherence Healing meditations, the high points of the events. METHODS: At all of the events in the first half of 2024, a tower of 40 RNGs in GCP 2.0 devices had been placed in the meditation room, constituting a local RNG network. Their data was compared to that of the hundreds of RNGs distributed across the globe, constituting a global network. All outputs were rigorously processed and tested to ensure randomness. In this exploratory study, each of the local and global networks were analyzed for Network Coherence, a measure of coherent activity across RNGs. RESULTS: During the 15 healing meditations, a significant correlation was observed between the Network Coherence of the global and local networks, with a mean correlation of 0.27 (p < 0.01). As this occurred only during healing meditations and not at other times, it suggests the highly focused attention and/or elevated emotional state across humans in the room may have measurable global impact, demonstrating the reach of a small coherent group. Also, combining the local RNG Network Coherence across all the healing meditation periods via a Stouffer's Z score resulted in a distinctive curve when plotted, resembling an evoked response. This suggests a mechanism whereby a coherently focused signal from the people in the room evokes a response from the RNGs similar to those observed in evoked potentials in the brain.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".