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Record W1996308684 · doi:10.4337/ejeep.2006.01.08

Do Heterodox Theories Have Anything in Common? A Post-Keynesian Point of View

2006· article· en· W1996308684 on OpenAlexaff
Marc Lavoie

Bibliographic record

VenueEuropean Journal of Economics and Economic Policies Intervention · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEconomicsAssertionHeterodox economicsPost-Keynesian economicsMarxist philosophyMainstream economicsMainstreamKeynesian economicsNeoclassical economicsPositive economicsRationalityApplied economicsEpistemologyLawPhilosophy

Abstract

fetched live from OpenAlex

The paper questions the wide-spread assertion that non-orthodox schools of thought in economics have only one thing in common – their rejection of mainstream (neoclassical) economics. The author shows by contrast that heterodox currents share some fundamental analytical insights. The paper focuses on a comparison of modern Marxist conceptions with those of Post-Keynesian economists, including the works of Kaleckians and Sraffians. This is shown by examining four fields: the issue of rationality (where the adjustment principle is explicitly accepted by important heterodox authors), price theory (with cost-plus pricing combined to some long-run adjustment), growth theory (where the Kaleckian model has been adopted by authors from all schools), and finally monetary theory (where authors from all backgrounds are successfully integrating real and monetary analysis by taking into account financial markets). The author concludes that mutual feedback between the various heterodox currents has been beneficial to all, despite an unavoidable hyper-specialisation.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.026
Scholarly communication0.0070.020
Open science0.0020.005
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.235
Teacher spread0.218 · 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 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

Citations56
Published2006
Admission routes1
Has abstractyes

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