The BRIC Nations and the Anatomy of Economic Development: The Core Tenets of Rule of Law
Bibliographic record
Abstract
Abstract The past decade has witnessed a revival in interest in the relationship between rule of law and economic development. Disenchantment with the universal model that emerged from the Washington Consensus era led to a shift in focus to a more pluralist approach in which policy prescriptions became tailored to each country’s socio-political context. The authors suggest that the pendulum may have swung to the other extreme, and that the newly ascendant pluralist approach may overemphasize pluralism at the expense of core rule of law principles. This paper examines the rule of law building efforts pursued by the BRIC countries to shed light on their strikingly different processes of economic transition. The authors argue that formal, informal, and transitional institutions have played distinct roles in these divergent economic development narratives. While informal and transitional institutions have facilitated growth during initial and intermediate phases of development, a degree of formalization across some institutions is critical to support the long-term development of a market economy. The authors set out the core tenets approach, an exploratory concept that emphasizes a malleable set of fundamental rule of law principles which exist alongside transition institutions to build trust in formal institutions as an economy advances.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".