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Record W2332392287 · doi:10.1515/gj-2014-0013

Rule of Law Reforms and Institutional Change Processes in Eastern DR Congo: Neo-institutional Economics vs <i>Multijuralism</i>

2015· article· en· W2332392287 on OpenAlexaff
Évelyne Jean-Bouchard

Bibliographic record

VenueGlobal Jurist · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNormativeRule of lawContext (archaeology)DemocracyInstitutional changeInstitutional economicsPolitical sciencePath dependenceLaw and developmentState (computer science)New institutional economicsWashington ConsensusOrder (exchange)Law and economicsEconomic systemPositive economicsLawEconomicsNeoclassical economicsPoliticsDevelopment studiesPublic administrationGeography

Abstract

fetched live from OpenAlex

Abstract In development approaches, the link between rule of law institutional reforms and economic development is theorized by neo-institutional economics (NIE). From an economic analysis of law, NIE interprets the institutional variable through its ability to reduce uncertainty. The analysis of the relationship between institutions and development then leads to the study of institutional and normative changes. In this context, authors are referring to path dependence theory in order to explain the recurrent failure of rule of law reforms. However, I will argue that while NIE, by referring to path dependence theory, acknowledges that reforms take place within a complex set of particularities, I suggest that the notion of multijuralism, elaborated by the French legal anthropologist Étienne Le Roy, is more appropriated to describe this set of particularities in an African context. Using empirical data collected during an anthropological study regarding women’s rights in Democratic Republic of Congo, we will see that normative changes usually occur on the margins of State institutions. In addition, the embedded norms considered by NIE immobile through time are actually much more fluid than it seems.

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)0.000
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: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.060
GPT teacher head0.245
Teacher spread0.185 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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