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Record W2051188950 · doi:10.1017/s002191180900062x

India and China: Governance Issues and Development

2009· article· en· W2051188950 on OpenAlexaboutno aff
Pranab Bardhan

Bibliographic record

VenueThe Journal of Asian Studies · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCorporate governanceBeijingPolitical scienceDemocracyPoliticsDevelopment economicsEconomyPolitical economyEconomic growthEconomicsLawManagement

Abstract

fetched live from OpenAlex

The world's two most populous countries each made international headlines in 2008, thanks to the Sichuan earthquake and Beijing Olympics, the Mumbai terror attacks, and reports of how the global financial crisis affected the Chinese and Indian economies. However, China and India were also seen in 2008 as making up, together, one of the ongoing development stories of the twenty-first century—and there are good reasons to think this trend will continue throughout 2009. Thanks to years of strikingly high growth rates for India and even higher ones for China, comparisons of the two countries have proliferated. This has resulted in a slew of books and articles, often aimed at investors, that worry over or enthuse about the way the “dragon” and the “elephant” have been upending or bringing new energy into the global economic order. Scholars immersed in the study of Asia often find these publications lacking in depth, but there is no question that economic shifts in the two countries and surging interest in their current trajectories are important. With this in mind, we decided this would be a useful time to revisit a familiar governance issue that has long been associated with joint discussions of China and India: the relationship between democracy and development. We invited economist Pranab Bardhan, author of a forthcoming book on the political economy of India and China titled Awakening Giants, Feet of Clay, to reflect on the subject, concentrating in particular on issues of governance—a topic he has explored in plenary and keynote addresses given everywhere from Montreal to Manchester, Beijing to Bogota, Canberra to Calcutta.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0030.009
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.239
Teacher spread0.214 · 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 designQualitative
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

Citations18
Published2009
Admission routes1
Has abstractyes

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