Uploaded July 2023 | Updated September 2026, 5 hours ago
Embeddings, a way to measure similarities between texts, are a recent gift of machine learning. They were used here to quantify similarities in short descriptions without dates of pairs in 7153 world events post-1600 AD as found in a provided database. There are 25,579,128 unique such pairs. The zodiacal placements of the charts were also calculated for either the actual date at midnight of the event or the same but using the start date for a multi-day event. Only Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, North Lunar Node, and South Lunar Node tropical degree placements from zero to 360 degrees were computed. Adding up the cosines of the differences between planetary placements for two events offers a neat and novel way of measuring similarities between these zodiacal placements for the two events. Thus, I have metrics for textual similarities and chart similarities. The imagery of these metrics plotted against each other suggests non-monotonic dependence. This study answers a precise question: what is the p-value for independence, i.e., nondependence, between the two metrics? The upper p-value for independence is less than 0.01 as computed by Hoeffding’s D measure. The conservative Monte Carlo approach to estimating the upper value of this p-value was necessary due to physical computation constraints. Thus, likelihood for the alternative to independence, namely dependence, between event textual similarities and event chart similarities is established. Data and Wolfram language code are included. Null Hypothesis: measures of event description and event astrology are independent. This is shown to be unlikely. The Alternative Hypothesis is thus supported: there is a dependency between the two measures. Code, data, and more information.
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The SSE provides a forum for original research into cutting edge and unconventional areas. Views and opinions belong only to the speakers, and are not necessarily endorsed by the SSE.
Embeddings, a way to measure similarities between texts, are a recent gift of machine learning. They were used here to quantify similarities in short descriptions without dates of pairs in 7153 world events post-1600 AD as found in a provided database. There are 25,579,128 unique such pairs. The zodiacal placements of the charts were also calculated for either the actual date at midnight of the event or the same but using the start date for a multi-day event. Only Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, North Lunar Node, and South Lunar Node tropical degree placements from zero to 360 degrees were computed. Adding up the cosines of the differences between planetary placements for two events offers a neat and novel way of measuring similarities between these zodiacal placements for the two events. Thus, I have metrics for textual similarities and chart similarities. The imagery of these metrics plotted against each other suggests non-monotonic dependence. This study answers a precise question: what is the p-value for independence, i.e., nondependence, between the two metrics? The upper p-value for independence is less than 0.01 as computed by Hoeffding’s D measure. The conservative Monte Carlo approach to estimating the upper value of this p-value was necessary due to physical computation constraints. Thus, likelihood for the alternative to independence, namely dependence, between event textual similarities and event chart similarities is established. Data and Wolfram language code are included. Null Hypothesis: measures of event description and event astrology are independent. This is shown to be unlikely. The Alternative Hypothesis is thus supported: there is a dependency between the two measures. Code, data, and more information.
Support the Society’s commitment to maintain an open professional forum for researchers at the edge of conventional science: https://linktr.ee/scientificexploration
The SSE provides a forum for original research into cutting edge and unconventional areas. Views and opinions belong only to the speakers, and are not necessarily endorsed by the SSE.
