Geopsychology of instrumental aggression: daily concurrence of global terrorism and solar-geomagnetic activity (1970-2018)
Bibliographic record
Abstract
Formal scientific study of the geopsychology of human aggression dates back at least a century and has consistently demonstrated a positive association between solar-geomagnetic activity and aggressive behaviour. Advances in the theories, methodologies, and practical applications of geopsychology could therefore contribute to collective efforts to comprehend, to forecast, and to develop interventions for aggressive behaviours such as those seen in terrorism. This requires a rigorous and precise estimate of the magnitude of association between solar-geomagnetic activity and aggression using a representative, contemporary sample of strictly-operationalized behaviour. Here we show that days in recent history (1970-2018) with the lowest levels of instrumental human aggression (number of casualty-associated terrorism incidents) also had the lowest levels of solar and geomagnetic activity, and that stepwise increases in human aggression were mirrored by progressive increases in solar activity. We used Bayesian methods robust to outliers and heterogeneity of variance to analyze the most comprehensive and contemporary global database of terrorism incidents available, which included more than 106,000 unique instances of instrumental aggression spanning 48 years. We conclude that there is a small, nonzero promotional effect of solar-geomagnetic activity on terrorism-related aggression. This may reflect the fact that solar-geomagnetic activity serves as a zeitgeber that coordinates the expression of instrumental aggression across an aggregation of susceptible individuals. We propose that many behaviours – even instrumental acts such as terrorism which are presumed to involve a degree of planning and intention – may be subject to subtle geopsychological induction or suppression.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".