Aristocratic Privilege. Exploiting "Good" Institutions
Bibliographic record
Abstract
According to North and Weingast (1989), institutions that protect bondholders' rights lower borrowing costs for the state and are therefore beneficial to both the state and the bondholders. In this paper we argue that such institutions may be so strong that bondholders can exploit them for their own benefit, not the state's. To prove this point, we focus on the (non-)conversion of French bonds during the second quarter of the 19th century. At the time, France was able to convert its bonds. In other words, the state could ask bondholders to choose between redemption at par or a new bond with a lower coupon. Even though improvements in French credit meant the state could benefit from converting its debt as early as May 1825, no conversion took place before March 1852. Had it occurred at the first date, the French state would have saved the equivalent of 2.7 years of debt service. Our analysis shows that the institutions prevailing at the time gave large bondholders the power to veto any conversion.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".