Bibliographic record
Abstract
Abstract This article describes and analyses a database on the dynamics of sociopolitical instability in the United States between 1780 and 2010. The database was constructed by digitizing data collected by previous researchers, supplemented by systematic searches of electronic media archives. It includes 1,590 political violence events such as riots, lynchings, and terrorism. Incidence of political violence fluctuated dramatically over the 230 years covered by the database, following a complex dynamical pattern. Spectral analysis detected two main oscillatory modes. The first is a very long-term – secular – cycle, taking the form of an instability wave during the second half of the 19th century, bracketed by two peaceful periods (the first quarter of the 19th century and the middle decades of the 20th century, respectively). The second is a 50-year oscillation superimposed on the secular cycle, with peaks around 1870, 1920, and 1970. The pattern of two periodicities superimposed on each other is characteristic of the dynamics of political instability in many historical societies, such as ancient Rome and medieval and early-modern England, France, and Russia. A possible explanation of this pattern, discussed in the article, is offered by the structural-demographic theory, which postulates that labor oversupply leads to falling living standards and elite overproduction, and those, in turn, cause a wave of prolonged and intense sociopolitical instability.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".