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Record W2493782397 · doi:10.1142/9789812839428_0005

ON EFFECTING INSTITUTIONAL CHANGE

2009· book-chapter· en· W2493782397 on OpenAlexaff
Sumon Majumdar, Sharun Mukand

Bibliographic record

VenueStatistical science and interdisciplinary research · 2009
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsQueen's University
FundersUniversity of Delhi
KeywordsInstitutional changeBusinessPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

AbstractIn this paper, we examine the role of policy intervention in engineering institutional change. More specifically, in a framework where the underlying political incentives determines the quality of a region's economic institutions, we examine the role of broad developmental policy in improving institutions and therbey welfare. Two effects emerge. First, by increasing political accountability, such policies encourage democratic governments to invest in good institutions – the incentive effect. However, such developmental policies also increase the incentive of the rentier elite to tighten their grip on political institutions. In some cases, this latter political control effect can outweigh the former incentive effect, and result in an overall deterioration of institutional quality. However, it may also indirectly encourage the elite to modernize. Taking this possibility into account, the framework has the potential to explain a diverse set of possibilites as a result of developmental policy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.007
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.188
GPT teacher head0.387
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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
Published2009
Admission routes1
Has abstractyes

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