Bibliographic record
Abstract
A fundamental requirement of market economies is the security of ownership \nclaims to property. Yet history is littered with cases of challenges to these \nclaims. A large literature has found contradictory evidence for the effect of \nincome and income inequality on revolt, possibly due to omitted variable bias. \nThe primary innovation of the paper is to tackle this problem in two ways. \nFirst, it introduces a new panel data set derived from surveys of revolutionary \nsupport across one-quarter of a million randomly sampled individuals. This \nallows one to control for unobserved fixed effects. Second, the estimated \nregressions are based on a choice-theoretic model of revolt that also helps us \nto choose an instrument set. After controlling for personal characteristics, \ncountry and year fixed effects, more people are found to favor revolt when \ninequality is high and their net incomes are low. An increase in inequality \nequivalent to a shift from Belgium to the US is predicted to increase support \nfor revolt by 6.3 percentage points. An increase in net income of $US 3330 (in \n1985 constant dollars) decreases revolutionary support by the same amount. \nThe results indicate that ‘going for growth’ can buy a nation out of revolt.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.019 |
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".