Optimal Size of Rebellions: Trade-off Between Large Group and Maintaining Secrecy
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it