Bibliographic record
Abstract
This paper explores the cascading influence of revolutionary moments on democracy and inequality, not at home, but across borders. We use data on revolutions and other social upheavals over the past 120 years and examine their cross-national impact on a range of variables in neighboring countries. Engaging with debates on whether substantial democracy and equality increases require extraordinary circumstances, our research investigates whether revolutionary activities induce consequential spillovers, such as policy concessions from elites in neighboring contexts. In exploring spillover effects, the paper examines how significant events in one nation influence social life in adjacent ones. It encompasses an analysis of 171 countries over two centuries, connecting data on revolution with democracy and equality metrics, and hypothesizing that elite fear of revolutionary contagion may necessitate democracy and equality concessions to mitigate potential uprisings. Findings suggest neighboring revolutions positively impact domestic democracy and equality levels. We observe significant increases in an index of democracy and two indices of economic egalitarianism, although one of the egalitarianism measures is robust to all model specifications. Additionally, we find that isolated "protest-led ousters" can moderately increase suffrage and one of our indices of egalitarianism, while coups do not seem to impact democracy or inequality variables. By examining various upheaval types and outcomes across time and space, the study illuminates the causal relationship between global mobilizations and local changes, providing insights into how global events inform domestic outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".