Anomalous entropic effects in physical systems associated with collective consciousness
Bibliographic record
Abstract
Beginning in 1998, a network of electronic random number generators located around the world has continuously recorded samples of truly random bits. The resulting data were used to explore a hypothesis that predicts the emergence of anomalous structure in randomness correlated with events that attract widespread human attention. A formal experiment testing this hypothesis from 1998 to 2015 found a highly significant deviation from chance expectation. However, the duration of the selected events comprised less than 5% of all data available through 2022, so the present analysis examined the full database to see if emergence of nonrandom structure was limited to those events, or if it was reflective of a persistent, if subtle, relationship between collective mind and matter. Two analytical methods were used to study emergent structure in time-series data: Multiscale entropy and a novel deconvolution technique. Both methods provided evidence consistent with the hypothesis, suggesting that some aspect of collective consciousness appears to be anomalously associated with aspects of the physical world.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".