The Predictability of Synchronicity Experience: Results from a Survey of Jungian Analysts
Bibliographic record
Abstract
Fibonacci time patterns may predict future synchronistic events (SEs) by forecasting nonlinear dynamical interactions. This study examined if there were differences between observed distributions of Fibonacci time patterns matching SEs compared to expected distributions based on chance. An online survey link was e-mailed to a random sample of Jungian analysts drawn from membership lists of the International Association for Analytical Psychology (IAAP). Two experiments tested the hypothesis that Fibonacci algorithms would predict increased synchronicity matches compared to chance. The two Fibonacci algorithms studied were a golden section model (GSM) and harmonic model (HM). Participants reported a total of 41 synchronicities. Statistical analysis showed a significant difference (p < .001) between observed and expected frequencies of matches based on chance for the HM algorithm, and no significant difference in matches predicted by the GSM algorithm. Synchronicity dynamics were found to exhibit a horizon of predictability between ±34 and ±89 days. The article discusses, among other issues, what these findings might mean for theoretical explanations of synchronicity and clinical practice.
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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 | 0.002 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".