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Record W2552952685 · doi:10.1515/ldr-2016-0013

The BRIC Nations and the Anatomy of Economic Development: The Core Tenets of Rule of Law

2016· article· en· W2552952685 on OpenAlexaff
Nandini Ramanujam, Nicholas Caivano

Bibliographic record

VenueThe Law and Development Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMcGill University
Fundersnot available
KeywordsRule of lawBRICPolitical scienceWashington ConsensusLaw and developmentContext (archaeology)Pluralism (philosophy)Political economyPoliticsEconomic systemSociologyLaw and economicsLawEconomicsDevelopment studiesChina

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.246
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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