MétaCan
Menu
Back to cohort
Record W3140432522

The Making of Miracles in Indian States: Andhra Pradesh, Bihar, and Gujarat

2015· article· en· W3140432522 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOUP Catalogue · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureGeographyLandlocked countryPovertySocioeconomicsEconomic growthPolitical scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

Growth miracles typically have been studied at the country level. The Making of Miracles in Indian States breaks from that tradition and studies three growth miracles in India at the level of the state: Andhra Pradesh, Bihar, and Gujarat. These are three of the largest and most diverse states in India. Andhra Pradesh is situated in the south of India, Bihar in the east, and Gujarat in the west. Bihar is the poorest among all states in India, Gujarat the third richest among the largest eighteen states, and Andhra Pradesh in the middle. Andhra Pradesh and Gujarat have long coastal lines while Bihar is landlocked. Yet, all of these states have grown at rates exceeding 8% for an entire decade in the 21st century. Despite many differences in the initial conditions, several common threads tie the high-growth experiences of the three states. First, accelerated growth has permitted acceleration in the growth of development expenditures in all three states, which has helped improve connectivity to markets. Alongside this growth, poverty has seen accelerated decline. Second, the composition of growth matters. Growth in high-value commodities such as fruits and vegetables, commercial crops, dairy, and animal husbandry in Andhra Pradesh and Gujarat has led to accelerated reduction in rural poverty. However, the failure of labor-intensive industry has stunted the migration of workers out of agriculture into industry. Third, the quality of leadership that brings improved governance with it is central to improved outcomes in the states. Visionary leaders---Chandrababu Naidu in Andhra Pradesh, Nitish Kumar in Bihar, and Narendra Modi in Gujarat---played critical roles in the making of all three miracles. Fourth, the three studies also bring out the importance of pro-market reforms and the adoption of technology in development. Finally, the studies show that good economics is also good politics: voters reward the chief ministers who bring about significant improvement to the people's lives. Available in OSO: Contributors to this volume - Rahul Ahluwalia, University of British Columbia, Vancouver. Archana Dholakia, Gujarat Institute of Development Research, Ahmedabad, Gujarat Ravindra Dholakia, Indian Institute of Management, Ahmedabad, Gujarat. Mudit Kapoor, Indian School of Business, Hyderabad, Andhra Pradesh. Arnab Mukherji, Indian Institute of Management, Bangalore, Karnataka. Anjan Mukherji, National Institute of Public Finance and Policy, New Delhi. Arvind Panagariya, Columbia University, New York, New York. M. Govinda Rao, Member, Fourteenth Finance Commission, New Delhi.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.240
Teacher spread0.204 · 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