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Record W3151056430 · doi:10.3386/w23845

East Asian Financial and Economic Development

2017· report· en· W3151056430 on OpenAlexaff
Randall Mørck, Bernard Yeung

Bibliographic record

VenueNational Bureau of Economic Research · 2017
Typereport
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEast AsiaBusinessFinanceEconomicsFinancial systemChinaGeographyArchaeology

Abstract

fetched live from OpenAlex

Japan, an isolated, backward country in the 1860s, industrialized rapidly to become a major industrial power by the 1930s. South Korea, among the world's poorest countries in the 1960s, joined the ranks of First World economies in little over a single generation. China now seems poised to follow a similar trajectory. All three cases highlight the importance of marginalized traditional elites, intensive early investment in education, a degree of economic openness, free markets, equity financing, early-stage coordination of firms in diverse industries via arrangements such as business groups, and political institutions capable of curbing the power of families grown wealthy in early-stage rapid development to make way for prosperity sustained by efficient resource allocation to high-productivity firms.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.313
GPT teacher head0.493
Teacher spread0.180 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2017
Admission routes1
Has abstractyes

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