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Record W3043859218 · doi:10.4337/9781786439550.00022

Is macro in crisis?

2020· book-chapter· en· W3043859218 on OpenAlexaboutno aff
Sheila Dow

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2020
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamConventionPositive economicsSociologyPolitical scienceEpistemologyEconomicsSocial scienceLaw

Abstract

fetched live from OpenAlex

The contributions of Marc Lavoie and Mario Seccareccia to macroeconomics are many, important and various. These contributions extend beyond their own research to their leadership in fostering others’ research. They have exercised this leadership, not only through their editing activities (most recently Lavoie and Seccareccia, 2017), but also in creating a supportive and productive academic environment at the University of Ottawa. I benefited from this myself first in 1983 when I was invited to present a seminar there. For the first time, I encountered impatience with what was then the required convention for heterodox economists of discussing the mainstream account of a topic before moving on to the heterodox alternative. This was a liberating experience. Lavoie and Seccareccia share a fundamental concern to tailor economic analysis to addressing pressing socio-economic problems. Rather than being constrained, as is typical among mainstream economists, by internal methodological concerns, Lavoie and Seccareccia have pursued methodologies according to external methodological concerns, choosing whatever best suits the real policy problem at hand. They are methodological pluralists, allowing for a range of approaches to analysing a complex, evolving reality, while (as is proper for methodological pluralists) arguing strongly for the relative merits of their own chosen approach while critiquing alternatives (as in Seccareccia’s, 1988, critique of idealisation in mainstream economics, and Lavoie’s, 2018, critique of DSGE modelling). They also accordingly explore relevant developments in the history of economic thought.

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), Scholarly communication, 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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.048
GPT teacher head0.216
Teacher spread0.169 · 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
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

Citations0
Published2020
Admission routes1
Has abstractyes

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