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Record W1907136017 · doi:10.4337/ejeep.2015.02.07

Teaching monetary theory and monetary policy implementation after the crisis*

2015· article· en· W1907136017 on OpenAlexaff
Marc Lavoie

Bibliographic record

VenueEuropean Journal of Economics and Economic Policies Intervention · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsMainstreamEconomicsKeynesian economicsFinancial crisisMainstream economicsExcellenceQuantity theory of moneyMonetary policyPositive economicsNeoclassical economicsApplied economicsEpistemologyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

The author reflects on the state of macroeconomic theory, and more specifically on how monetary economics is being taught in the aftermath of the global financial crisis. Whereas heterodox macroeconomic theory is very much alive due to the influx of a large number of contributions and contributors, the latter still have a hard time finding positions in the academic world, as journal and departmental ranking exercises have restricted so-called standards of excellence to the neoclassical approach. The crisis has not yet induced mainstream economists to open up to different approaches that put more emphasis on realistic features than on imaginary ones based on neoclassical micro-foundations. As an exemplar, the chapters on money and banking in two first-year textbooks are being examined, an orthodox one by Greg Mankiw and a heterodox one by Neva Goodwin et al. Unsurprisingly there is little to be learned about the crisis from Mankiw's book, while the book by Goodwin et al. devotes a large amount of space to the causes and consequences of the crisis. Still, Goodwin and her co-authors are not heterodox enough: they are less heterodox than a number of central bankers when it comes to a number of key features of monetary theory.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.268
Teacher spread0.240 · 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.

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

Citations6
Published2015
Admission routes1
Has abstractyes

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