Does planets’ position in zodiac constellations drive the occurrence of human wars? A retrospective test of non-natal astrology
Bibliographic record
Abstract
The lack of descriptive and predictive effectiveness of natal astrology revealed thus far does not rule out the possibility that the position of planets in specific areas of the background sky influences human behavior regardless of birth date. This study compiled a list of war events that occurred throughout the twentieth century, which were used as proxies for a state of violence in the global human community at particular moments in time. Ecliptic longitude values of Mercury, Venus, Mars, Jupiter, Saturn, and the Moon at the beginning day of each war were distributed across 12 sections of the ecliptic of 30º span each. East longitude boundary of Aries was set starting at 23° from the vernal equinox in agreement with sidereal astrology. A total of 210 war events were collected for the analysis. The observed and expected war counts when planets were transiting through each zodiacal constellation were statistically similar in all cases (P values from 0.076 to 0.658). In addition, the occurrence of wars when two planets were in conjunction in specific zodiac constellations was in agreement with their expected positions according to regular planetary movement. Independently of astrology beliefs and planetary dynamics, wars showed a weak trend, though significant, towards occurring more frequently later than earlier in a year (slope: 0.78 [95% CI: 0.12, 1.48]; r 2=0.408; P=0.025). In conclusion, even though the study design favored the astrological theses, no relationship between war triggering and the positions of planets transiting through zodiacal constellations was found.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".