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Record W4210421316 · doi:10.4337/roke.2022.01.01

The Godley-Tobin Memorial Lecture

2022· article· en· W4210421316 on OpenAlexaff
Marc Lavoie

Bibliographic record

VenueReview of Keynesian Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEconomicsPost-Keynesian economicsKeynesian economicsPortfolioMicrofoundationsMonetary policyTobin's qMonetary economicsMacroeconomicsFinancial economics

Abstract

fetched live from OpenAlex

This paper offers a comparison of the macroeconomic views held by Wynne Godley and James Tobin. Both authors were more concerned than their contemporaries with monetary matters. Both authors contributed, in different ways, to the stock–flow consistent approach, with Tobin providing to Godley the portfolio analysis he was missing. Both authors held Keynesian policy positions, but both were accused at times of not being Keynesian enough. While Tobin stuck with Neoclassical theory, Godley rejected it as he could never make any sense of it. The differences between these two authors are particularly evident when dealing with the traverse of economic activity from the short run to the long run. The biggest difference has to do with their conceptions of banking: Tobin argued that banks are barely different from other financial intermediaries, essentially providing a portfolio choice, and ultimately he relies on a variable multiplier view tied to the fractional-reserve theory of banking; by contrast, Godley emphasized the credit-creating ability of banks and their essential role in an economy where production takes time and where inventories are needed, with central banks providing reserves on demand, at the interest rate of their choice, as argued by central bankers today.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0140.006

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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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 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

Citations28
Published2022
Admission routes1
Has abstractyes

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