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Record W1524941107 · doi:10.1007/978-1-137-09971-6_5

Markets, Human Capital and Inequality: Evidence from Rural China

2002· book-chapter· en· W1524941107 on OpenAlexaff
Dwayne Benjamin, Loren Brandt, Paul Glewwe, Li Guo

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2002
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomicsArgument (complex analysis)Planned economyDistribution (mathematics)Market economyInvestment (military)InequalityIncome distributionFactor marketChinaEconomic inequalityEconomic system

Abstract

fetched live from OpenAlex

Beginning in the 1980s, almost all of the socialist countries replaced their planned economies with economic systems that relied heavily on market forces to determine the production and allocation of goods and services. This transformation has affected the lives of nearly two billion people. Historically, the two main arguments in favour of planned economies were that they are more productive in the long run (because they avoided the inherent instability of market forces, and were able to mobilize more resources for investment than a decentralized system), and that they provide a more equitable distribution of income. The experience of both socialist and market economies in the twentieth century decisively rejects the first argument; it would be hard to find observers of almost any persuasion who claim that planned economies are more productive or more efficient than market economies. Yet the second argument may well be valid; planned economies may indeed be more equitable than market economies. This raises the possibility that some societies may wish to retain at least some of the policies of planned economies, despite their inefficiencies, to maintain a more equitable distribution of income. Consequently, for countries that abandoned planning in favour of the market an important policy issue is the extent to which this policy shift has increased inequality.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.028
GPT teacher head0.267
Teacher spread0.239 · 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 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

Citations31
Published2002
Admission routes1
Has abstractyes

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