THE PARADOX OF RULE OF LAW REFORMS: HOW EARLY REFORMS CAN CREATE OBSTACLES TO FUTURE ONES
Bibliographic record
Abstract
In their most recent book, Rule of Law Reform and Development: Charting the Fragile Path of Progress, Michael Trebilcock and Ron Daniels show how rule of law reforms have a mixed – not to say disappointing – track record of successes. Their diagnosis is that social–historical–cultural factors and resistance from interest groups are two of the main obstacles to reform. This essay explores these two obstacles in greater depth. With respect to social–historical–cultural factors, my main argument is that early rule of law reforms can create values, practices, and attitudes that may become impediments to future reforms. On the political economy front, these early reforms can strengthen interest groups that will block future reforms. As a consequence, policy makers face a paradox: robust rule of law reforms early on may undermine broader reform efforts by reducing the possibility of other necessary reforms down the road, creating a reform trap. I illustrate the paradox with a Brazilian case study and discuss possible strategies to address this challenge.
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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 | 0.065 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.048 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".