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Record W4366829548 · doi:10.1007/s40803-023-00190-4

Market-Engaging Institutions: The Rule of Law, Resilience and Responsiveness in an Era of Institutional Flux

2023· article· en· W4366829548 on OpenAlexafffund
Nandini Ramanujam, Francesca Farrington

Bibliographic record

VenueHague Journal on the Rule of Law · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsMcGill University
FundersMcGill University
KeywordsRule of lawEconomic systemEconomicsState (computer science)PoliticsMarket economyBusinessLawPolitical science

Abstract

fetched live from OpenAlex

Abstract This article analyses the institutional conditions required to support a strategic state in being responsive to the changing demands of a market-economy, whilst maintaining a credible commitment to long-term policy goals. This article identifies a key pillar of a market economy that we believe is crucial to promoting inclusive economic growth; we term these institutions market-engaging institutions. We propose that market-engaging institutions may form a bridge between the flexibility required by a dynamic market economy and the stability demanded by the rule of law. We define market-engaging institutions as those institutions that facilitate greater political participation for marginalized groups, manage technological disruptions, and support human capital formation. Examples include social partnership agreements, collective bargaining coverage, trade union membership, education and training services, and research and development programmes. We suggest that mobilizing these institutions necessitates credible commitment. Further, we argue that through its commitment to the non-arbitrary administration of general rules the rule of law is an essential condition for signalling the state’s credible commitment. However, at times the requirement for the state to be flexible to the changing needs of market actors may conflict with the rule of law’s demand for constancy and stability. This article examines the delicate balancing act required to sustain a strategic, responsive, and credible state in an era of institutional flux.

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)0.004
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 score0.942
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.068
GPT teacher head0.333
Teacher spread0.265 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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