The Fibonacci Life‐Chart Method (FLCM) as a foundation for Carl Jung's theory of synchronicity
Bibliographic record
Abstract
Since the scientific method requires events to be subject to controlled examination it would seem that synchronicities are not scientifically investigable. Jung speculated that because these incredible events are like the random sparks of a firefly they cannot be pinned down. However, doubting Jung's doubts, the author provides a possible method of elucidating these seemingly random and elusive events. The author draws on a new method, designated the Fibonacci Life-Chart Method (FLCM), which categorizes phase transitions and Phi fractal scaling in human development based on the occurrence of Fibonacci numbers in biological cell division and self-organizing systems. The FLCM offers an orientation towards psychological experience that may have relevance to Jung's theory of synchronicity in which connections are deemed to be intrinsically meaningful rather than demonstrable consequences of cause and effect. In such a model synchronistic events can be seen to be, as the self-organizing system enlarges, manifestations of self-organized critical moments and Phi fractal scaling. Recommendations for future studies include testing the results of the FLCM using case reports of synchronistic and spiritual experiences.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".