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Record W2122404728 · doi:10.7202/601018ar

Investissement, progrès technique et croissance économique

2009· article· en· W2122404728 on OpenAlexvenueno aff
Paul Davenport

Bibliographic record

VenueL Actualité économique · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsInvestment functionPost-Keynesian economicsInvestment (military)Keynesian economicsGrowth theoryDistribution (mathematics)UnemploymentIncome distributionNeoclassical economicsGrowth modelGrowth accountingEndogenous growth theoryEconometricsMacroeconomicsMathematical economicsMathematicsProductivityHuman capitalMarket economyProduction (economics)Law

Abstract

fetched live from OpenAlex

This paper combines a critical review of the current state of the theory of economic growth with some suggestions for new directions in growth theory. The development of the neoclassical theory of growth and distribution is surveyed, with emphasis on the distribution theory of J.B. Clark, the regression analysis of C.W. Cobb and P.H. Douglas, the growth accounting of R.M. Solow and E.F. Denison, and the reswitching controversy, involving critical contributions by Joan Robinson, Piero Sraffa, and Luigi Pasinetti. Neoclassical growth models, including vintage models, are based on the theoretical separation of investment and technical change, which leads to the curious conclusion that investment is not a central part of the growth process. Post-Keynesian growth models, such as those of Nicholas Kaldor and John Cornwall, deny that such a separation is theoretically or empirically meaningful, and instead put investment at the heart of the growth process. The paper constructs a growth model along post-Keynesian lines, in which the growth rate, the distribution of income, and the normal unemployment rate are endogenous functions of the propensity to invest.

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)0.002
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.076
GPT teacher head0.270
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2009
Admission routes1
Has abstractyes

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