Optimal Size of Rebellions: Trade-off Between Large Group and Maintaining Secrecy
Bibliographic record
Abstract
This paper studies a model of regime change in which a rebel leader seeking to mobilize supporters faces a trade-off between increasing the rebel group’s size and risking information leaks. I find that repressing a rebellion via collective punishment — whereby not only rebel participants but also those individuals who knew about (but did not report) the rebellion are punished — may result in a smaller-sized rebel group than in the case of targeted punishment, under which only the actual rebel participants are punished. Authorities prefer collective punishment to induce information leaks from rebel groups, however one consequence of adopting collective punishment is that citizens are then put to side with the insurgency, which in turn reduces the regime’s odds of survival. My findings also indicate that, whereas targeted punishment helps prevent rebellion by ordinary citizens who simply desire policy changes, collective punishment helps prevent a revolution staged by those who are driven by pecuniary rewards. Finally, if authorities compete with rebel leaders for support by threatening retribution against non-supporters, then both parties prefer using relatively harsh methods as a means of forcing civilians to choose sides.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".